R2: Delphi Comparison Using Phecode-Based ICD Mapping¶
This notebook creates a principled comparison between Aladynoulli and Delphi-2M by:
- Disease Name → Phenotype Names: Using our
major_diseasesmapping fromevaluate_major_disease_wsex_rolling_tte.py - Phenotype Names → Phecodes: Looking up phenotype names in the Phecode mapping file
- Phecodes → ICD Codes: Extracting all ICD codes that map to those Phecodes
- ICD Codes → Delphi Results: Extracting Delphi's "no gap" (t0) predictions for those ICD codes
- Comparison: Our t0 predictions (
washout_0yr_results.csv) vs Delphi's t0 predictions
This ensures we use the actual Phecode→ICD aggregation that our model uses, rather than manual approximations.
Key Insight¶
This comparison is more principled than manual ICD mappings because:
- It uses the same Phecode aggregation logic that our model uses
- It captures all ICD codes that contribute to each Phecode
- It ensures fair comparison by matching on the same disease definitions
In [2]:
import pandas as pd
import pandas as pd
import numpy as np
from pathlib import Path
import sys
In [3]:
# Load major_diseases mapping from evaluate_major_disease_wsex_rolling_tte.py
major_diseases = {
'ASCVD': ['Myocardial infarction', 'Coronary atherosclerosis', 'Other acute and subacute forms of ischemic heart disease',
'Unstable angina (intermediate coronary syndrome)', 'Angina pectoris', 'Other chronic ischemic heart disease, unspecified'],
'Diabetes': ['Type 2 diabetes'],
'Atrial_Fib': ['Atrial fibrillation and flutter'],
'CKD': ['Chronic renal failure [CKD]', 'Chronic Kidney Disease, Stage III'],
'All_Cancers': ['Colon cancer', 'Cancer of bronchus; lung', 'Cancer of prostate', 'Malignant neoplasm of bladder', 'Secondary malignant neoplasm','Secondary malignant neoplasm of digestive systems', 'Secondary malignant neoplasm of liver'],
'Stroke': ['Cerebral artery occlusion, with cerebral infarction', 'Cerebral ischemia'],
'Heart_Failure': ['Congestive heart failure (CHF) NOS', 'Heart failure NOS'],
'Pneumonia': ['Pneumonia', 'Bacterial pneumonia', 'Pneumococcal pneumonia'],
'COPD': ['Chronic airway obstruction', 'Emphysema', 'Obstructive chronic bronchitis'],
'Osteoporosis': ['Osteoporosis NOS'],
'Anemia': ['Iron deficiency anemias, unspecified or not due to blood loss', 'Other anemias'],
'Colorectal_Cancer': ['Colon cancer', 'Malignant neoplasm of rectum, rectosigmoid junction, and anus'],
'Breast_Cancer': ['Breast cancer [female]', 'Malignant neoplasm of female breast'],
'Prostate_Cancer': ['Cancer of prostate'],
'Lung_Cancer': ['Cancer of bronchus; lung'],
'Bladder_Cancer': ['Malignant neoplasm of bladder'],
'Secondary_Cancer': ['Secondary malignant neoplasm', 'Secondary malignancy of lymph nodes', 'Secondary malignancy of respiratory organs', 'Secondary malignant neoplasm of digestive systems'],
'Depression': ['Major depressive disorder'],
'Anxiety': ['Anxiety disorder'],
'Bipolar_Disorder': ['Bipolar'],
'Rheumatoid_Arthritis': ['Rheumatoid arthritis'],
'Psoriasis': ['Psoriasis vulgaris'],
'Ulcerative_Colitis': ['Ulcerative colitis'],
'Crohns_Disease': ['Regional enteritis'],
'Asthma': ['Asthma'],
'Parkinsons': ["Parkinson's disease"],
'Multiple_Sclerosis': ['Multiple sclerosis'],
'Thyroid_Disorders': ['Thyrotoxicosis with or without goiter', 'Secondary hypothyroidism', 'Hypothyroidism NOS']
}
print(f"✓ Loaded {len(major_diseases)} disease mappings")
print(f"\nExample mappings:")
for disease, phenotypes in list(major_diseases.items())[:3]:
print(f" {disease}: {phenotypes}")
✓ Loaded 28 disease mappings Example mappings: ASCVD: ['Myocardial infarction', 'Coronary atherosclerosis', 'Other acute and subacute forms of ischemic heart disease', 'Unstable angina (intermediate coronary syndrome)', 'Angina pectoris', 'Other chronic ischemic heart disease, unspecified'] Diabetes: ['Type 2 diabetes'] Atrial_Fib: ['Atrial fibrillation and flutter']
Step 2: Load Phecode Mapping File¶
Load the Phecode mapping file that contains ICD-10 → Phecode mappings. This file also contains phenotype names that we can match against.
Step 3: Map Phenotype Names → Phecodes → ICD Codes¶
For each disease, find the Phecodes that match its phenotype names, then extract all ICD codes that map to those Phecodes.
In [5]:
import pyreadr
phecode_mapping_path = '/Users/sarahurbut/aladynoulli2/noulli_mapped_phecode_icd10cm.rds'
if Path(phecode_mapping_path).exists():
try:
result = pyreadr.read_r(phecode_mapping_path)
phecode_mapping_df = result[None]
print(f"✓ Loaded Phecode mapping from RDS: {len(phecode_mapping_df)} rows")
print(f"\nColumns in mapping file: {phecode_mapping_df.columns.tolist()}")
print(f"\nFirst few rows:")
print(phecode_mapping_df.head())
except Exception as e:
print(f"⚠️ Error loading RDS: {e}")
phecode_mapping_df = None
else:
print(f"⚠️ File not found: {phecode_mapping_path}")
phecode_mapping_df = None
✓ Loaded Phecode mapping from RDS: 14046 rows Columns in mapping file: ['phecode', 'ICD10', 'phenotype'] First few rows: phecode ICD10 phenotype 0 8.5 A0229 Bacterial enteritis 1 8.5 A039 Bacterial enteritis 2 8.5 A050 Bacterial enteritis 3 8.5 A030 Bacterial enteritis 4 8.5 A053 Bacterial enteritis
In [6]:
# Map disease → phenotype → ICD10 codes directly from phecode_mapping_df
# For each disease, find rows where 'phenotype' column matches phenotype names, then extract 'diag_icd10' codes
disease_to_icd_mapping = {}
if phecode_mapping_df is not None:
# Use correct column names from the mapping file
phenotype_col = 'phenotype' # Column with phenotype names
icd10_col = 'ICD10' # Column with ICD10 codes
for disease_name, phenotype_list in major_diseases.items():
matched_icd10_codes = set()
matched_phenotypes = []
# For each phenotype in the disease, find matching rows in phecode_mapping_df
for phenotype in phenotype_list:
# Match phenotype name in the 'phenotype' column (case-insensitive)
matches = phecode_mapping_df[
phecode_mapping_df[phenotype_col].str.contains(phenotype, case=False, na=False, regex=False)
]
if len(matches) > 0:
# Extract unique ICD10 codes from matching rows
icd10_codes = matches[icd10_col].dropna().unique()
matched_icd10_codes.update(icd10_codes)
matched_phenotypes.append(phenotype)
print(f" ✓ {phenotype}: {len(icd10_codes)} ICD10 codes")
disease_to_icd_mapping[disease_name] = {
'phenotypes': phenotype_list,
'matched_phenotypes': matched_phenotypes,
'icd10_codes': sorted(list(matched_icd10_codes))
}
print(f"{disease_name}: {len(matched_phenotypes)}/{len(phenotype_list)} phenotypes matched, {len(matched_icd10_codes)} unique ICD10 codes")
print(f"\n✓ Mapped {len(disease_to_icd_mapping)} diseases to ICD10 codes")
# Show summary
print("\nSummary:")
for disease, mapping in list(disease_to_icd_mapping.items())[:5]:
print(f" {disease}: {len(mapping['icd10_codes'])} ICD10 codes")
if len(mapping['icd10_codes']) > 0:
print(f" Examples: {mapping['icd10_codes'][:5]}")
else:
print("⚠️ Cannot create mapping without Phecode file")
✓ Myocardial infarction: 34 ICD10 codes
✓ Coronary atherosclerosis: 36 ICD10 codes
✓ Other acute and subacute forms of ischemic heart disease: 2 ICD10 codes
✓ Unstable angina (intermediate coronary syndrome): 1 ICD10 codes
✓ Angina pectoris: 4 ICD10 codes
✓ Other chronic ischemic heart disease, unspecified: 6 ICD10 codes
ASCVD: 6/6 phenotypes matched, 83 unique ICD10 codes
✓ Type 2 diabetes: 23 ICD10 codes
Diabetes: 1/1 phenotypes matched, 23 unique ICD10 codes
✓ Atrial fibrillation and flutter: 1 ICD10 codes
Atrial_Fib: 1/1 phenotypes matched, 1 unique ICD10 codes
✓ Chronic renal failure [CKD]: 2 ICD10 codes
✓ Chronic Kidney Disease, Stage III: 1 ICD10 codes
CKD: 2/2 phenotypes matched, 3 unique ICD10 codes
✓ Colon cancer: 23 ICD10 codes
✓ Cancer of bronchus; lung: 33 ICD10 codes
✓ Cancer of prostate: 3 ICD10 codes
✓ Malignant neoplasm of bladder: 12 ICD10 codes
✓ Secondary malignant neoplasm: 35 ICD10 codes
✓ Secondary malignant neoplasm of digestive systems: 7 ICD10 codes
✓ Secondary malignant neoplasm of liver: 2 ICD10 codes
All_Cancers: 7/7 phenotypes matched, 106 unique ICD10 codes
✓ Cerebral artery occlusion, with cerebral infarction: 73 ICD10 codes
✓ Cerebral ischemia: 16 ICD10 codes
Stroke: 2/2 phenotypes matched, 89 unique ICD10 codes
✓ Congestive heart failure (CHF) NOS: 2 ICD10 codes
✓ Heart failure NOS: 13 ICD10 codes
Heart_Failure: 2/2 phenotypes matched, 15 unique ICD10 codes
✓ Pneumonia: 31 ICD10 codes
✓ Bacterial pneumonia: 23 ICD10 codes
✓ Pneumococcal pneumonia: 2 ICD10 codes
Pneumonia: 3/3 phenotypes matched, 31 unique ICD10 codes
✓ Chronic airway obstruction: 1 ICD10 codes
✓ Emphysema: 11 ICD10 codes
✓ Obstructive chronic bronchitis: 3 ICD10 codes
COPD: 3/3 phenotypes matched, 15 unique ICD10 codes
✓ Osteoporosis NOS: 4 ICD10 codes
Osteoporosis: 1/1 phenotypes matched, 4 unique ICD10 codes
✓ Iron deficiency anemias, unspecified or not due to blood loss: 4 ICD10 codes
✓ Other anemias: 6 ICD10 codes
Anemia: 2/2 phenotypes matched, 10 unique ICD10 codes
✓ Colon cancer: 23 ICD10 codes
✓ Malignant neoplasm of rectum, rectosigmoid junction, and anus: 17 ICD10 codes
Colorectal_Cancer: 2/2 phenotypes matched, 40 unique ICD10 codes
✓ Breast cancer [female]: 17 ICD10 codes
✓ Malignant neoplasm of female breast: 38 ICD10 codes
Breast_Cancer: 2/2 phenotypes matched, 55 unique ICD10 codes
✓ Cancer of prostate: 3 ICD10 codes
Prostate_Cancer: 1/1 phenotypes matched, 3 unique ICD10 codes
✓ Cancer of bronchus; lung: 33 ICD10 codes
Lung_Cancer: 1/1 phenotypes matched, 33 unique ICD10 codes
✓ Malignant neoplasm of bladder: 12 ICD10 codes
Bladder_Cancer: 1/1 phenotypes matched, 12 unique ICD10 codes
✓ Secondary malignant neoplasm: 35 ICD10 codes
✓ Secondary malignancy of lymph nodes: 10 ICD10 codes
✓ Secondary malignancy of respiratory organs: 9 ICD10 codes
✓ Secondary malignant neoplasm of digestive systems: 7 ICD10 codes
Secondary_Cancer: 4/4 phenotypes matched, 54 unique ICD10 codes
✓ Major depressive disorder: 21 ICD10 codes
Depression: 1/1 phenotypes matched, 21 unique ICD10 codes
✓ Anxiety disorder: 4 ICD10 codes
Anxiety: 1/1 phenotypes matched, 4 unique ICD10 codes
✓ Bipolar: 45 ICD10 codes
Bipolar_Disorder: 1/1 phenotypes matched, 45 unique ICD10 codes
✓ Rheumatoid arthritis: 406 ICD10 codes
Rheumatoid_Arthritis: 1/1 phenotypes matched, 406 unique ICD10 codes
✓ Psoriasis vulgaris: 6 ICD10 codes
Psoriasis: 1/1 phenotypes matched, 6 unique ICD10 codes
✓ Ulcerative colitis: 33 ICD10 codes
Ulcerative_Colitis: 1/1 phenotypes matched, 33 unique ICD10 codes
✓ Regional enteritis: 32 ICD10 codes
Crohns_Disease: 1/1 phenotypes matched, 32 unique ICD10 codes
✓ Asthma: 9 ICD10 codes
Asthma: 1/1 phenotypes matched, 9 unique ICD10 codes
✓ Parkinson's disease: 2 ICD10 codes
Parkinsons: 1/1 phenotypes matched, 2 unique ICD10 codes
✓ Multiple sclerosis: 1 ICD10 codes
Multiple_Sclerosis: 1/1 phenotypes matched, 1 unique ICD10 codes
✓ Thyrotoxicosis with or without goiter: 15 ICD10 codes
✓ Secondary hypothyroidism: 2 ICD10 codes
✓ Hypothyroidism NOS: 1 ICD10 codes
Thyroid_Disorders: 3/3 phenotypes matched, 18 unique ICD10 codes
✓ Mapped 28 diseases to ICD10 codes
Summary:
ASCVD: 83 ICD10 codes
Examples: ['I20', 'I200', 'I201', 'I208', 'I209']
Diabetes: 23 ICD10 codes
Examples: ['E11', 'E110', 'E1100', 'E1101', 'E116']
Atrial_Fib: 1 ICD10 codes
Examples: ['I48']
CKD: 3 ICD10 codes
Examples: ['N18', 'N183', 'N189']
All_Cancers: 106 ICD10 codes
Examples: ['C18', 'C180', 'C181', 'C182', 'C183']
In [7]:
# Load Delphi supplementary table
delphi_supp_paths = [
'/Users/sarahurbut/Downloads/41586_2025_9529_MOESM3_ESM.csv',
'/Users/sarahurbut/aladynoulli2/claudefile/output/delphi_supplementary.csv',
]
delphi_supp = None
for path in delphi_supp_paths:
if Path(path).exists():
try:
delphi_supp = pd.read_csv(path)
print(f"✓ Loaded Delphi supplementary table: {len(delphi_supp)} rows")
print(f" Columns: {delphi_supp.columns.tolist()}")
break
except Exception as e:
print(f"⚠️ Error loading {path}: {e}")
if delphi_supp is None:
print("⚠️ Delphi supplementary table not found!")
print(" Expected locations:")
for path in delphi_supp_paths:
print(f" {path}")
else:
print(f"\nFirst few rows:")
print(delphi_supp.head())
✓ Loaded Delphi supplementary table: 1270 rows
Columns: ['Index', 'Name', 'ICD-10 Chapter', 'ICD-10 Chapter (short)', 'Colour', 'AUC Female, (no gap)', 'AUC Male, (no gap)', 'AUC Female, (1 year gap)', 'AUC Male, (1 year gap)', 'N tokens, training', 'N tokens, validation']
First few rows:
Index Name ICD-10 Chapter ICD-10 Chapter (short) \
0 0 Padding Technical Technical
1 1 Healthy Technical Technical
2 2 Female Sex Sex
3 3 Male Sex Sex
4 4 BMI low Smoking, Alcohol and BMI Smoking, Alcohol and BMI
Colour AUC Female, (no gap) AUC Male, (no gap) \
0 #2a52be NaN NaN
1 #2a52be NaN NaN
2 #bcbd22 NaN NaN
3 #bcbd22 NaN NaN
4 #9467bd NaN NaN
AUC Female, (1 year gap) AUC Male, (1 year gap) N tokens, training \
0 NaN NaN 0
1 NaN NaN 0
2 NaN NaN 218608
3 NaN NaN 183444
4 NaN NaN 37912
N tokens, validation
0 0
1 0
2 54768
3 45670
4 9493
Step 5: Extract Delphi Results Using ICD10 Codes¶
Match the ICD10 codes from our phecode mapping against Delphi's table to extract AUC results.
In [8]:
# Extract Delphi AUCs for each disease using ICD10 codes from phecode mapping
# Match ICD10 codes from disease_to_icd_mapping against Delphi table
delphi_results = []
if delphi_supp is not None and 'disease_to_icd_mapping' in locals():
# Identify Delphi column names
name_col = None
auc_0gap_female_col = None
auc_0gap_male_col = None
for col in delphi_supp.columns:
col_lower = col.lower()
if 'name' in col_lower and name_col is None:
name_col = col
if 'auc' in col_lower and 'female' in col_lower and 'no gap' in col_lower:
auc_0gap_female_col = col
if 'auc' in col_lower and 'male' in col_lower and 'no gap' in col_lower:
auc_0gap_male_col = col
print(f"Delphi columns identified:")
print(f" Name: {name_col}")
print(f" AUC Female (0 gap): {auc_0gap_female_col}")
print(f" AUC Male (0 gap): {auc_0gap_male_col}")
if name_col:
for disease_name, mapping_info in disease_to_icd_mapping.items():
icd10_codes = mapping_info['icd10_codes']
if len(icd10_codes) == 0:
print(f"⚠️ {disease_name}: No ICD10 codes found from phecode mapping")
continue
# Match ICD10 codes against Delphi table
# Delphi Name column contains ICD codes like "I21 Acute myocardial infarction"
matching_rows = []
matched_icd10_codes = []
for icd10_code in icd10_codes:
# Extract first 3 characters (Delphi uses 3-character codes like "I21", "E11", "N18")
# Our phecode mapping gives 4-character codes like "I236", "E114", "N183"
icd10_base = icd10_code[:3] # e.g., "I236" -> "I23", "E114" -> "E11"
# Match ICD codes that start with the 3-character pattern (e.g., "I21" matches "I21 Acute myocardial infarction")
matches = delphi_supp[
delphi_supp[name_col].str.contains(f'^{icd10_base}', regex=True, case=False, na=False)
]
if len(matches) > 0:
matching_rows.append(matches)
matched_icd10_codes.append(icd10_code)
print(f" ✓ {disease_name}: ICD10 {icd10_code} ({icd10_base}) → {len(matches)} Delphi matches")
if len(matching_rows) > 0:
# Combine all matching rows
combined = pd.concat(matching_rows).drop_duplicates()
# Create one row per Delphi ICD code match (1-to-many structure)
import re
for idx, row in combined.iterrows():
# Extract ICD code and name from Delphi row
icd_code = None
delphi_name = None
if name_col in row.index:
name_val = str(row[name_col])
delphi_name = name_val
icd_match = re.match(r'^([A-Z]\d{2})', name_val)
if icd_match:
icd_code = icd_match.group(1)
# Collect both female and male AUCs
female_auc = None
male_auc = None
if auc_0gap_female_col and auc_0gap_female_col in row.index and pd.notna(row[auc_0gap_female_col]):
female_auc = row[auc_0gap_female_col]
if auc_0gap_male_col and auc_0gap_male_col in row.index and pd.notna(row[auc_0gap_male_col]):
male_auc = row[auc_0gap_male_col]
# Average male and female if both available, otherwise use available one
if female_auc is not None and male_auc is not None:
avg_auc = (female_auc + male_auc) / 2
elif female_auc is not None:
avg_auc = female_auc
elif male_auc is not None:
avg_auc = male_auc
else:
continue # Skip if no AUC available
# Create one row per Delphi ICD code match
delphi_results.append({
'Disease': disease_name,
'Delphi_t0': avg_auc,
'Delphi_ICD_code': icd_code if icd_code else '',
'Delphi_name': delphi_name if delphi_name else '',
'Delphi_female_auc': female_auc if female_auc is not None else np.nan,
'Delphi_male_auc': male_auc if male_auc is not None else np.nan,
'N_ICD10_codes_matched': len(matched_icd10_codes),
'N_ICD10_codes_total': len(icd10_codes),
'Matched_ICD10_codes': ', '.join(matched_icd10_codes[:5]) + ('...' if len(matched_icd10_codes) > 5 else '')
})
else:
print(f"⚠️ {disease_name}: No Delphi matches found for {len(icd10_codes)} ICD10 codes")
delphi_df = pd.DataFrame(delphi_results)
print(f"\n" + "="*80)
print(f"✓ Extracted Delphi results: {len(delphi_df)} ICD code matches across {len(set(delphi_df['Disease']))} diseases")
print("="*80)
if len(delphi_df) > 0:
print(f"\nDelphi results summary (1-to-many structure):")
print(f" Total Delphi ICD code matches: {len(delphi_df)}")
print(f" Unique diseases: {len(set(delphi_df['Disease']))}")
print(f"\nExample (showing all ICD codes for first disease):")
first_disease = delphi_df['Disease'].iloc[0]
print(delphi_df[delphi_df['Disease'] == first_disease][['Disease', 'Delphi_ICD_code', 'Delphi_t0', 'Delphi_name']].to_string(index=False))
else:
print("⚠️ Cannot extract Delphi results without Delphi table or disease_to_icd_mapping")
Delphi columns identified: Name: Name AUC Female (0 gap): AUC Female, (no gap) AUC Male (0 gap): AUC Male, (no gap) ✓ ASCVD: ICD10 I20 (I20) → 1 Delphi matches ✓ ASCVD: ICD10 I200 (I20) → 1 Delphi matches ✓ ASCVD: ICD10 I201 (I20) → 1 Delphi matches ✓ ASCVD: ICD10 I208 (I20) → 1 Delphi matches ✓ ASCVD: ICD10 I209 (I20) → 1 Delphi matches ✓ ASCVD: ICD10 I21 (I21) → 1 Delphi matches ✓ ASCVD: ICD10 I210 (I21) → 1 Delphi matches ✓ ASCVD: ICD10 I2101 (I21) → 1 Delphi matches ✓ ASCVD: ICD10 I2102 (I21) → 1 Delphi matches ✓ ASCVD: ICD10 I2109 (I21) → 1 Delphi matches ✓ ASCVD: ICD10 I211 (I21) → 1 Delphi matches ✓ ASCVD: ICD10 I2111 (I21) → 1 Delphi matches ✓ ASCVD: ICD10 I2119 (I21) → 1 Delphi matches ✓ ASCVD: ICD10 I212 (I21) → 1 Delphi matches ✓ ASCVD: ICD10 I2121 (I21) → 1 Delphi matches ✓ ASCVD: ICD10 I2129 (I21) → 1 Delphi matches ✓ ASCVD: ICD10 I213 (I21) → 1 Delphi matches ✓ ASCVD: ICD10 I214 (I21) → 1 Delphi matches ✓ ASCVD: ICD10 I219 (I21) → 1 Delphi matches ✓ ASCVD: ICD10 I21A (I21) → 1 Delphi matches ✓ ASCVD: ICD10 I21A1 (I21) → 1 Delphi matches ✓ ASCVD: ICD10 I21A9 (I21) → 1 Delphi matches ✓ ASCVD: ICD10 I22 (I22) → 1 Delphi matches ✓ ASCVD: ICD10 I220 (I22) → 1 Delphi matches ✓ ASCVD: ICD10 I221 (I22) → 1 Delphi matches ✓ ASCVD: ICD10 I222 (I22) → 1 Delphi matches ✓ ASCVD: ICD10 I228 (I22) → 1 Delphi matches ✓ ASCVD: ICD10 I229 (I22) → 1 Delphi matches ✓ ASCVD: ICD10 I230 (I23) → 1 Delphi matches ✓ ASCVD: ICD10 I231 (I23) → 1 Delphi matches ✓ ASCVD: ICD10 I232 (I23) → 1 Delphi matches ✓ ASCVD: ICD10 I233 (I23) → 1 Delphi matches ✓ ASCVD: ICD10 I236 (I23) → 1 Delphi matches ✓ ASCVD: ICD10 I237 (I23) → 1 Delphi matches ✓ ASCVD: ICD10 I238 (I23) → 1 Delphi matches ✓ ASCVD: ICD10 I240 (I24) → 1 Delphi matches ✓ ASCVD: ICD10 I241 (I24) → 1 Delphi matches ✓ ASCVD: ICD10 I248 (I24) → 1 Delphi matches ✓ ASCVD: ICD10 I249 (I24) → 1 Delphi matches ✓ ASCVD: ICD10 I25 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I251 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I2510 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I25110 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I25111 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I25118 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I25119 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I252 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I255 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I256 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I25700 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I25701 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I25708 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I25709 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I25710 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I25711 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I25718 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I25719 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I25720 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I25721 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I25728 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I25729 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I25730 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I25731 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I25738 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I25739 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I25790 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I25791 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I25798 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I25799 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I258 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I25810 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I2582 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I2583 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I2584 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I2589 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I259 (I25) → 1 Delphi matches ✓ ASCVD: ICD10 I510 (I51) → 1 Delphi matches ✓ ASCVD: ICD10 I513 (I51) → 1 Delphi matches ✓ Diabetes: ICD10 E11 (E11) → 1 Delphi matches ✓ Diabetes: ICD10 E110 (E11) → 1 Delphi matches ✓ Diabetes: ICD10 E1100 (E11) → 1 Delphi matches ✓ Diabetes: ICD10 E1101 (E11) → 1 Delphi matches ✓ Diabetes: ICD10 E116 (E11) → 1 Delphi matches ✓ Diabetes: ICD10 E1161 (E11) → 1 Delphi matches ✓ Diabetes: ICD10 E11610 (E11) → 1 Delphi matches ✓ Diabetes: ICD10 E11618 (E11) → 1 Delphi matches ✓ Diabetes: ICD10 E1162 (E11) → 1 Delphi matches ✓ Diabetes: ICD10 E11620 (E11) → 1 Delphi matches ✓ Diabetes: ICD10 E11621 (E11) → 1 Delphi matches ✓ Diabetes: ICD10 E11622 (E11) → 1 Delphi matches ✓ Diabetes: ICD10 E11628 (E11) → 1 Delphi matches ✓ Diabetes: ICD10 E1163 (E11) → 1 Delphi matches ✓ Diabetes: ICD10 E11630 (E11) → 1 Delphi matches ✓ Diabetes: ICD10 E11638 (E11) → 1 Delphi matches ✓ Diabetes: ICD10 E1164 (E11) → 1 Delphi matches ✓ Diabetes: ICD10 E11641 (E11) → 1 Delphi matches ✓ Diabetes: ICD10 E11649 (E11) → 1 Delphi matches ✓ Diabetes: ICD10 E1165 (E11) → 1 Delphi matches ✓ Diabetes: ICD10 E1169 (E11) → 1 Delphi matches ✓ Diabetes: ICD10 E118 (E11) → 1 Delphi matches ✓ Diabetes: ICD10 E119 (E11) → 1 Delphi matches ✓ Atrial_Fib: ICD10 I48 (I48) → 1 Delphi matches ✓ CKD: ICD10 N18 (N18) → 1 Delphi matches ✓ CKD: ICD10 N183 (N18) → 1 Delphi matches ✓ CKD: ICD10 N189 (N18) → 1 Delphi matches ✓ All_Cancers: ICD10 C18 (C18) → 1 Delphi matches ✓ All_Cancers: ICD10 C180 (C18) → 1 Delphi matches ✓ All_Cancers: ICD10 C181 (C18) → 1 Delphi matches ✓ All_Cancers: ICD10 C182 (C18) → 1 Delphi matches ✓ All_Cancers: ICD10 C183 (C18) → 1 Delphi matches ✓ All_Cancers: ICD10 C184 (C18) → 1 Delphi matches ✓ All_Cancers: ICD10 C185 (C18) → 1 Delphi matches ✓ All_Cancers: ICD10 C186 (C18) → 1 Delphi matches ✓ All_Cancers: ICD10 C187 (C18) → 1 Delphi matches ✓ All_Cancers: ICD10 C188 (C18) → 1 Delphi matches ✓ All_Cancers: ICD10 C189 (C18) → 1 Delphi matches ✓ All_Cancers: ICD10 C260 (C26) → 1 Delphi matches ✓ All_Cancers: ICD10 C33 (C33) → 1 Delphi matches ✓ All_Cancers: ICD10 C34 (C34) → 1 Delphi matches ✓ All_Cancers: ICD10 C340 (C34) → 1 Delphi matches ✓ All_Cancers: ICD10 C3400 (C34) → 1 Delphi matches ✓ All_Cancers: ICD10 C3401 (C34) → 1 Delphi matches ✓ All_Cancers: ICD10 C3402 (C34) → 1 Delphi matches ✓ All_Cancers: ICD10 C341 (C34) → 1 Delphi matches ✓ All_Cancers: ICD10 C3410 (C34) → 1 Delphi matches ✓ All_Cancers: ICD10 C3411 (C34) → 1 Delphi matches ✓ All_Cancers: ICD10 C3412 (C34) → 1 Delphi matches ✓ All_Cancers: ICD10 C342 (C34) → 1 Delphi matches ✓ All_Cancers: ICD10 C343 (C34) → 1 Delphi matches ✓ All_Cancers: ICD10 C3430 (C34) → 1 Delphi matches ✓ All_Cancers: ICD10 C3431 (C34) → 1 Delphi matches ✓ All_Cancers: ICD10 C3432 (C34) → 1 Delphi matches ✓ All_Cancers: ICD10 C348 (C34) → 1 Delphi matches ✓ All_Cancers: ICD10 C3480 (C34) → 1 Delphi matches ✓ All_Cancers: ICD10 C3481 (C34) → 1 Delphi matches ✓ All_Cancers: ICD10 C3482 (C34) → 1 Delphi matches ✓ All_Cancers: ICD10 C349 (C34) → 1 Delphi matches ✓ All_Cancers: ICD10 C3490 (C34) → 1 Delphi matches ✓ All_Cancers: ICD10 C3491 (C34) → 1 Delphi matches ✓ All_Cancers: ICD10 C3492 (C34) → 1 Delphi matches ✓ All_Cancers: ICD10 C61 (C61) → 1 Delphi matches ✓ All_Cancers: ICD10 C67 (C67) → 1 Delphi matches ✓ All_Cancers: ICD10 C670 (C67) → 1 Delphi matches ✓ All_Cancers: ICD10 C671 (C67) → 1 Delphi matches ✓ All_Cancers: ICD10 C672 (C67) → 1 Delphi matches ✓ All_Cancers: ICD10 C673 (C67) → 1 Delphi matches ✓ All_Cancers: ICD10 C674 (C67) → 1 Delphi matches ✓ All_Cancers: ICD10 C675 (C67) → 1 Delphi matches ✓ All_Cancers: ICD10 C676 (C67) → 1 Delphi matches ✓ All_Cancers: ICD10 C677 (C67) → 1 Delphi matches ✓ All_Cancers: ICD10 C678 (C67) → 1 Delphi matches ✓ All_Cancers: ICD10 C679 (C67) → 1 Delphi matches ✓ All_Cancers: ICD10 C78 (C78) → 1 Delphi matches ✓ All_Cancers: ICD10 C784 (C78) → 1 Delphi matches ✓ All_Cancers: ICD10 C785 (C78) → 1 Delphi matches ✓ All_Cancers: ICD10 C786 (C78) → 1 Delphi matches ✓ All_Cancers: ICD10 C787 (C78) → 1 Delphi matches ✓ All_Cancers: ICD10 C788 (C78) → 1 Delphi matches ✓ All_Cancers: ICD10 C7880 (C78) → 1 Delphi matches ✓ All_Cancers: ICD10 C7889 (C78) → 1 Delphi matches ✓ All_Cancers: ICD10 C790 (C79) → 1 Delphi matches ✓ All_Cancers: ICD10 C7900 (C79) → 1 Delphi matches ✓ All_Cancers: ICD10 C7901 (C79) → 1 Delphi matches ✓ All_Cancers: ICD10 C7902 (C79) → 1 Delphi matches ✓ All_Cancers: ICD10 C791 (C79) → 1 Delphi matches ✓ All_Cancers: ICD10 C7910 (C79) → 1 Delphi matches ✓ All_Cancers: ICD10 C7911 (C79) → 1 Delphi matches ✓ All_Cancers: ICD10 C7919 (C79) → 1 Delphi matches ✓ All_Cancers: ICD10 C796 (C79) → 1 Delphi matches ✓ All_Cancers: ICD10 C7960 (C79) → 1 Delphi matches ✓ All_Cancers: ICD10 C7961 (C79) → 1 Delphi matches ✓ All_Cancers: ICD10 C7962 (C79) → 1 Delphi matches ✓ All_Cancers: ICD10 C797 (C79) → 1 Delphi matches ✓ All_Cancers: ICD10 C7970 (C79) → 1 Delphi matches ✓ All_Cancers: ICD10 C7971 (C79) → 1 Delphi matches ✓ All_Cancers: ICD10 C7972 (C79) → 1 Delphi matches ✓ All_Cancers: ICD10 C798 (C79) → 1 Delphi matches ✓ All_Cancers: ICD10 C7981 (C79) → 1 Delphi matches ✓ All_Cancers: ICD10 C7982 (C79) → 1 Delphi matches ✓ All_Cancers: ICD10 C7989 (C79) → 1 Delphi matches ✓ All_Cancers: ICD10 C799 (C79) → 1 Delphi matches ✓ All_Cancers: ICD10 D010 (D01) → 1 Delphi matches ✓ All_Cancers: ICD10 D022 (D02) → 1 Delphi matches ✓ All_Cancers: ICD10 D0220 (D02) → 1 Delphi matches ✓ All_Cancers: ICD10 D0221 (D02) → 1 Delphi matches ✓ All_Cancers: ICD10 D0222 (D02) → 1 Delphi matches ✓ All_Cancers: ICD10 D075 (D07) → 1 Delphi matches ✓ All_Cancers: ICD10 J910 (J91) → 1 Delphi matches ✓ Stroke: ICD10 G450 (G45) → 1 Delphi matches ✓ Stroke: ICD10 G451 (G45) → 1 Delphi matches ✓ Stroke: ICD10 G452 (G45) → 1 Delphi matches ✓ Stroke: ICD10 G458 (G45) → 1 Delphi matches ✓ Stroke: ICD10 G459 (G45) → 1 Delphi matches ✓ Stroke: ICD10 G460 (G46) → 1 Delphi matches ✓ Stroke: ICD10 G461 (G46) → 1 Delphi matches ✓ Stroke: ICD10 G462 (G46) → 1 Delphi matches ✓ Stroke: ICD10 I630 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I633 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I6330 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I6331 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63311 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63312 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63313 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63319 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I6332 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63321 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63322 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63323 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63329 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I6333 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63331 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63332 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63333 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63339 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I6334 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63341 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63342 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63343 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63349 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I6339 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I634 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I6340 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I6341 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63411 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63412 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63413 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63419 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I6342 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63421 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63422 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63423 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63429 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I6343 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63431 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63432 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63433 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63439 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I6344 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63441 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63442 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63443 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63449 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I6349 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I635 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I6350 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I6351 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63511 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63512 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63513 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63519 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I6352 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63521 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63522 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63523 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63529 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I6353 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63531 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63532 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63533 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63539 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I6354 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63541 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63542 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63543 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I63549 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I6359 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I636 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I638 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I639 (I63) → 1 Delphi matches ✓ Stroke: ICD10 I678 (I67) → 1 Delphi matches ✓ Stroke: ICD10 I6781 (I67) → 1 Delphi matches ✓ Stroke: ICD10 I6782 (I67) → 1 Delphi matches ✓ Stroke: ICD10 I67841 (I67) → 1 Delphi matches ✓ Stroke: ICD10 I67848 (I67) → 1 Delphi matches ✓ Stroke: ICD10 I6789 (I67) → 1 Delphi matches ✓ Stroke: ICD10 M4702 (M47) → 1 Delphi matches ✓ Heart_Failure: ICD10 I0981 (I09) → 1 Delphi matches ✓ Heart_Failure: ICD10 I50 (I50) → 1 Delphi matches ✓ Heart_Failure: ICD10 I501 (I50) → 1 Delphi matches ✓ Heart_Failure: ICD10 I508 (I50) → 1 Delphi matches ✓ Heart_Failure: ICD10 I5081 (I50) → 1 Delphi matches ✓ Heart_Failure: ICD10 I50810 (I50) → 1 Delphi matches ✓ Heart_Failure: ICD10 I50811 (I50) → 1 Delphi matches ✓ Heart_Failure: ICD10 I50812 (I50) → 1 Delphi matches ✓ Heart_Failure: ICD10 I50813 (I50) → 1 Delphi matches ✓ Heart_Failure: ICD10 I50814 (I50) → 1 Delphi matches ✓ Heart_Failure: ICD10 I5082 (I50) → 1 Delphi matches ✓ Heart_Failure: ICD10 I5083 (I50) → 1 Delphi matches ✓ Heart_Failure: ICD10 I5084 (I50) → 1 Delphi matches ✓ Heart_Failure: ICD10 I5089 (I50) → 1 Delphi matches ✓ Heart_Failure: ICD10 I509 (I50) → 1 Delphi matches ✓ Pneumonia: ICD10 A0222 (A02) → 1 Delphi matches ✓ Pneumonia: ICD10 A202 (A20) → 1 Delphi matches ✓ Pneumonia: ICD10 A221 (A22) → 1 Delphi matches ✓ Pneumonia: ICD10 A310 (A31) → 1 Delphi matches ✓ Pneumonia: ICD10 A420 (A42) → 1 Delphi matches ✓ Pneumonia: ICD10 A430 (A43) → 1 Delphi matches ✓ Pneumonia: ICD10 A481 (A48) → 1 Delphi matches ✓ Pneumonia: ICD10 A78 (A78) → 1 Delphi matches ✓ Pneumonia: ICD10 B583 (B58) → 1 Delphi matches ✓ Pneumonia: ICD10 J13 (J13) → 1 Delphi matches ✓ Pneumonia: ICD10 J14 (J14) → 1 Delphi matches ✓ Pneumonia: ICD10 J150 (J15) → 1 Delphi matches ✓ Pneumonia: ICD10 J152 (J15) → 1 Delphi matches ✓ Pneumonia: ICD10 J1520 (J15) → 1 Delphi matches ✓ Pneumonia: ICD10 J15212 (J15) → 1 Delphi matches ✓ Pneumonia: ICD10 J1529 (J15) → 1 Delphi matches ✓ Pneumonia: ICD10 J153 (J15) → 1 Delphi matches ✓ Pneumonia: ICD10 J154 (J15) → 1 Delphi matches ✓ Pneumonia: ICD10 J155 (J15) → 1 Delphi matches ✓ Pneumonia: ICD10 J156 (J15) → 1 Delphi matches ✓ Pneumonia: ICD10 J157 (J15) → 1 Delphi matches ✓ Pneumonia: ICD10 J158 (J15) → 1 Delphi matches ✓ Pneumonia: ICD10 J159 (J15) → 1 Delphi matches ✓ Pneumonia: ICD10 J160 (J16) → 1 Delphi matches ✓ Pneumonia: ICD10 J168 (J16) → 1 Delphi matches ✓ Pneumonia: ICD10 J18 (J18) → 1 Delphi matches ✓ Pneumonia: ICD10 J181 (J18) → 1 Delphi matches ✓ Pneumonia: ICD10 J188 (J18) → 1 Delphi matches ✓ Pneumonia: ICD10 J189 (J18) → 1 Delphi matches ✓ COPD: ICD10 J43 (J43) → 1 Delphi matches ✓ COPD: ICD10 J430 (J43) → 1 Delphi matches ✓ COPD: ICD10 J431 (J43) → 1 Delphi matches ✓ COPD: ICD10 J432 (J43) → 1 Delphi matches ✓ COPD: ICD10 J438 (J43) → 1 Delphi matches ✓ COPD: ICD10 J439 (J43) → 1 Delphi matches ✓ COPD: ICD10 J44 (J44) → 1 Delphi matches ✓ COPD: ICD10 J440 (J44) → 1 Delphi matches ✓ COPD: ICD10 J441 (J44) → 1 Delphi matches ✓ COPD: ICD10 J449 (J44) → 1 Delphi matches ✓ COPD: ICD10 J981 (J98) → 1 Delphi matches ✓ COPD: ICD10 J9811 (J98) → 1 Delphi matches ✓ COPD: ICD10 J9819 (J98) → 1 Delphi matches ✓ COPD: ICD10 J982 (J98) → 1 Delphi matches ✓ COPD: ICD10 J983 (J98) → 1 Delphi matches ✓ Osteoporosis: ICD10 M81 (M81) → 1 Delphi matches ✓ Osteoporosis: ICD10 M810 (M81) → 1 Delphi matches ✓ Osteoporosis: ICD10 M816 (M81) → 1 Delphi matches ✓ Osteoporosis: ICD10 M818 (M81) → 1 Delphi matches ✓ Anemia: ICD10 D50 (D50) → 1 Delphi matches ✓ Anemia: ICD10 D501 (D50) → 1 Delphi matches ✓ Anemia: ICD10 D508 (D50) → 1 Delphi matches ✓ Anemia: ICD10 D509 (D50) → 1 Delphi matches ✓ Anemia: ICD10 D6182 (D61) → 1 Delphi matches ✓ Anemia: ICD10 D64 (D64) → 1 Delphi matches ✓ Anemia: ICD10 D644 (D64) → 1 Delphi matches ✓ Anemia: ICD10 D648 (D64) → 1 Delphi matches ✓ Anemia: ICD10 D6489 (D64) → 1 Delphi matches ✓ Anemia: ICD10 D649 (D64) → 1 Delphi matches ✓ Colorectal_Cancer: ICD10 C18 (C18) → 1 Delphi matches ✓ Colorectal_Cancer: ICD10 C180 (C18) → 1 Delphi matches ✓ Colorectal_Cancer: ICD10 C181 (C18) → 1 Delphi matches ✓ Colorectal_Cancer: ICD10 C182 (C18) → 1 Delphi matches ✓ Colorectal_Cancer: ICD10 C183 (C18) → 1 Delphi matches ✓ Colorectal_Cancer: ICD10 C184 (C18) → 1 Delphi matches ✓ Colorectal_Cancer: ICD10 C185 (C18) → 1 Delphi matches ✓ Colorectal_Cancer: ICD10 C186 (C18) → 1 Delphi matches ✓ Colorectal_Cancer: ICD10 C187 (C18) → 1 Delphi matches ✓ Colorectal_Cancer: ICD10 C188 (C18) → 1 Delphi matches ✓ Colorectal_Cancer: ICD10 C189 (C18) → 1 Delphi matches ✓ Colorectal_Cancer: ICD10 C19 (C19) → 1 Delphi matches ✓ Colorectal_Cancer: ICD10 C20 (C20) → 1 Delphi matches ✓ Colorectal_Cancer: ICD10 C210 (C21) → 1 Delphi matches ✓ Colorectal_Cancer: ICD10 C211 (C21) → 1 Delphi matches ✓ Colorectal_Cancer: ICD10 C260 (C26) → 1 Delphi matches ✓ Colorectal_Cancer: ICD10 D010 (D01) → 1 Delphi matches ✓ Colorectal_Cancer: ICD10 D011 (D01) → 1 Delphi matches ✓ Colorectal_Cancer: ICD10 D012 (D01) → 1 Delphi matches ✓ Colorectal_Cancer: ICD10 D013 (D01) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C50 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C5001 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C50011 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C50012 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C50019 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C5011 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C50111 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C50112 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C50119 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C5021 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C50211 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C50212 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C50219 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C5031 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C50311 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C50312 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C50319 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C5041 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C50411 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C50412 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C50419 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C5051 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C50511 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C50512 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C50519 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C5061 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C50611 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C50612 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C50619 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C508 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C5081 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C50811 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C50812 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C50819 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C5091 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C50911 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C50912 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 C50919 (C50) → 1 Delphi matches ✓ Breast_Cancer: ICD10 D05 (D05) → 1 Delphi matches ✓ Breast_Cancer: ICD10 D050 (D05) → 1 Delphi matches ✓ Breast_Cancer: ICD10 D0500 (D05) → 1 Delphi matches ✓ Breast_Cancer: ICD10 D0501 (D05) → 1 Delphi matches ✓ Breast_Cancer: ICD10 D0502 (D05) → 1 Delphi matches ✓ Breast_Cancer: ICD10 D051 (D05) → 1 Delphi matches ✓ Breast_Cancer: ICD10 D0510 (D05) → 1 Delphi matches ✓ Breast_Cancer: ICD10 D0511 (D05) → 1 Delphi matches ✓ Breast_Cancer: ICD10 D0512 (D05) → 1 Delphi matches ✓ Breast_Cancer: ICD10 D058 (D05) → 1 Delphi matches ✓ Breast_Cancer: ICD10 D0580 (D05) → 1 Delphi matches ✓ Breast_Cancer: ICD10 D0581 (D05) → 1 Delphi matches ✓ Breast_Cancer: ICD10 D0582 (D05) → 1 Delphi matches ✓ Breast_Cancer: ICD10 D059 (D05) → 1 Delphi matches ✓ Breast_Cancer: ICD10 D0590 (D05) → 1 Delphi matches ✓ Breast_Cancer: ICD10 D0591 (D05) → 1 Delphi matches ✓ Breast_Cancer: ICD10 D0592 (D05) → 1 Delphi matches ✓ Prostate_Cancer: ICD10 C61 (C61) → 1 Delphi matches ✓ Prostate_Cancer: ICD10 D075 (D07) → 1 Delphi matches ✓ Lung_Cancer: ICD10 C33 (C33) → 1 Delphi matches ✓ Lung_Cancer: ICD10 C34 (C34) → 1 Delphi matches ✓ Lung_Cancer: ICD10 C340 (C34) → 1 Delphi matches ✓ Lung_Cancer: ICD10 C3400 (C34) → 1 Delphi matches ✓ Lung_Cancer: ICD10 C3401 (C34) → 1 Delphi matches ✓ Lung_Cancer: ICD10 C3402 (C34) → 1 Delphi matches ✓ Lung_Cancer: ICD10 C341 (C34) → 1 Delphi matches ✓ Lung_Cancer: ICD10 C3410 (C34) → 1 Delphi matches ✓ Lung_Cancer: ICD10 C3411 (C34) → 1 Delphi matches ✓ Lung_Cancer: ICD10 C3412 (C34) → 1 Delphi matches ✓ Lung_Cancer: ICD10 C342 (C34) → 1 Delphi matches ✓ Lung_Cancer: ICD10 C343 (C34) → 1 Delphi matches ✓ Lung_Cancer: ICD10 C3430 (C34) → 1 Delphi matches ✓ Lung_Cancer: ICD10 C3431 (C34) → 1 Delphi matches ✓ Lung_Cancer: ICD10 C3432 (C34) → 1 Delphi matches ✓ Lung_Cancer: ICD10 C348 (C34) → 1 Delphi matches ✓ Lung_Cancer: ICD10 C3480 (C34) → 1 Delphi matches ✓ Lung_Cancer: ICD10 C3481 (C34) → 1 Delphi matches ✓ Lung_Cancer: ICD10 C3482 (C34) → 1 Delphi matches ✓ Lung_Cancer: ICD10 C349 (C34) → 1 Delphi matches ✓ Lung_Cancer: ICD10 C3490 (C34) → 1 Delphi matches ✓ Lung_Cancer: ICD10 C3491 (C34) → 1 Delphi matches ✓ Lung_Cancer: ICD10 C3492 (C34) → 1 Delphi matches ✓ Lung_Cancer: ICD10 D022 (D02) → 1 Delphi matches ✓ Lung_Cancer: ICD10 D0220 (D02) → 1 Delphi matches ✓ Lung_Cancer: ICD10 D0221 (D02) → 1 Delphi matches ✓ Lung_Cancer: ICD10 D0222 (D02) → 1 Delphi matches ✓ Bladder_Cancer: ICD10 C67 (C67) → 1 Delphi matches ✓ Bladder_Cancer: ICD10 C670 (C67) → 1 Delphi matches ✓ Bladder_Cancer: ICD10 C671 (C67) → 1 Delphi matches ✓ Bladder_Cancer: ICD10 C672 (C67) → 1 Delphi matches ✓ Bladder_Cancer: ICD10 C673 (C67) → 1 Delphi matches ✓ Bladder_Cancer: ICD10 C674 (C67) → 1 Delphi matches ✓ Bladder_Cancer: ICD10 C675 (C67) → 1 Delphi matches ✓ Bladder_Cancer: ICD10 C676 (C67) → 1 Delphi matches ✓ Bladder_Cancer: ICD10 C677 (C67) → 1 Delphi matches ✓ Bladder_Cancer: ICD10 C678 (C67) → 1 Delphi matches ✓ Bladder_Cancer: ICD10 C679 (C67) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C77 (C77) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C770 (C77) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C771 (C77) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C772 (C77) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C773 (C77) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C774 (C77) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C775 (C77) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C778 (C77) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C779 (C77) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C78 (C78) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C780 (C78) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C7800 (C78) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C7801 (C78) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C7802 (C78) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C781 (C78) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C782 (C78) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C783 (C78) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C7830 (C78) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C7839 (C78) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C784 (C78) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C785 (C78) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C786 (C78) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C787 (C78) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C788 (C78) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C7880 (C78) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C7889 (C78) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C790 (C79) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C7900 (C79) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C7901 (C79) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C7902 (C79) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C791 (C79) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C7910 (C79) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C7911 (C79) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C7919 (C79) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C796 (C79) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C7960 (C79) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C7961 (C79) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C7962 (C79) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C797 (C79) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C7970 (C79) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C7971 (C79) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C7972 (C79) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C798 (C79) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C7981 (C79) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C7982 (C79) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C7989 (C79) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 C799 (C79) → 1 Delphi matches ✓ Secondary_Cancer: ICD10 J910 (J91) → 1 Delphi matches ✓ Depression: ICD10 F32 (F32) → 1 Delphi matches ✓ Depression: ICD10 F320 (F32) → 1 Delphi matches ✓ Depression: ICD10 F321 (F32) → 1 Delphi matches ✓ Depression: ICD10 F322 (F32) → 1 Delphi matches ✓ Depression: ICD10 F323 (F32) → 1 Delphi matches ✓ Depression: ICD10 F324 (F32) → 1 Delphi matches ✓ Depression: ICD10 F325 (F32) → 1 Delphi matches ✓ Depression: ICD10 F328 (F32) → 1 Delphi matches ✓ Depression: ICD10 F3289 (F32) → 1 Delphi matches ✓ Depression: ICD10 F329 (F32) → 1 Delphi matches ✓ Depression: ICD10 F33 (F33) → 1 Delphi matches ✓ Depression: ICD10 F330 (F33) → 1 Delphi matches ✓ Depression: ICD10 F331 (F33) → 1 Delphi matches ✓ Depression: ICD10 F332 (F33) → 1 Delphi matches ✓ Depression: ICD10 F333 (F33) → 1 Delphi matches ✓ Depression: ICD10 F334 (F33) → 1 Delphi matches ✓ Depression: ICD10 F3340 (F33) → 1 Delphi matches ✓ Depression: ICD10 F3341 (F33) → 1 Delphi matches ✓ Depression: ICD10 F3342 (F33) → 1 Delphi matches ✓ Depression: ICD10 F338 (F33) → 1 Delphi matches ✓ Depression: ICD10 F339 (F33) → 1 Delphi matches ✓ Anxiety: ICD10 F064 (F06) → 1 Delphi matches ✓ Anxiety: ICD10 F413 (F41) → 1 Delphi matches ✓ Anxiety: ICD10 F418 (F41) → 1 Delphi matches ✓ Anxiety: ICD10 F419 (F41) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F301 (F30) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F3010 (F30) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F3011 (F30) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F3012 (F30) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F3013 (F30) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F302 (F30) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F303 (F30) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F304 (F30) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F308 (F30) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F309 (F30) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F31 (F31) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F310 (F31) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F311 (F31) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F3110 (F31) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F3111 (F31) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F3112 (F31) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F3113 (F31) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F312 (F31) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F313 (F31) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F3130 (F31) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F3131 (F31) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F3132 (F31) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F314 (F31) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F315 (F31) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F316 (F31) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F3160 (F31) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F3161 (F31) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F3162 (F31) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F3163 (F31) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F3164 (F31) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F317 (F31) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F3170 (F31) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F3171 (F31) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F3172 (F31) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F3173 (F31) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F3174 (F31) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F3175 (F31) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F3176 (F31) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F3177 (F31) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F3178 (F31) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F318 (F31) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F3181 (F31) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F3189 (F31) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F319 (F31) → 1 Delphi matches ✓ Bipolar_Disorder: ICD10 F3281 (F32) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M050 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0500 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0501 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05011 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05012 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05019 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0502 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05021 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05022 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05029 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0503 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05031 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05032 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05039 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0504 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05041 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05042 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05049 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0505 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05051 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05052 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05059 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0506 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05061 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05062 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05069 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0507 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05071 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05072 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05079 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0509 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M051 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0510 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0511 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05111 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05112 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05119 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0512 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05121 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05122 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05129 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0513 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05131 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05132 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05139 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0514 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05141 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05142 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05149 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0515 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05151 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05152 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05159 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0516 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05161 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05162 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05169 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0517 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05171 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05172 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05179 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0519 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M052 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0520 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0521 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05211 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05212 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05219 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0522 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05221 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05222 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05229 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0523 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05231 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05232 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05239 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0524 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05241 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05242 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05249 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0525 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05251 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05252 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05259 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0526 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05261 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05262 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05269 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0527 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05271 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05272 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05279 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0529 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0530 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0531 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05311 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05312 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05319 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0532 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05321 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05322 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05329 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0533 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05331 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05332 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05339 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0534 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05341 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05342 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05349 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0535 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05351 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05352 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05359 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0536 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05361 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05362 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05369 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0537 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05371 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05372 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05379 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0539 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0540 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0541 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05411 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05412 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05419 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0542 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05421 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05422 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05429 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0543 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05431 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05432 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05439 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0544 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05441 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05442 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05449 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0545 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05451 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05452 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05459 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0546 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05461 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05462 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05469 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0547 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05471 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05472 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05479 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0549 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0550 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0551 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05511 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05512 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05519 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0552 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05521 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05522 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05529 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0553 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05531 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05532 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05539 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0554 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05541 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05542 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05549 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0555 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05551 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05552 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05559 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0556 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05561 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05562 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05569 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0557 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05571 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05572 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05579 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0559 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M056 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0560 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0561 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05611 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05612 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05619 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0562 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05621 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05622 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05629 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0563 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05631 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05632 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05639 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0564 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05641 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05642 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05649 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0565 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05651 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05652 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05659 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0566 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05661 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05662 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05669 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0567 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05671 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05672 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05679 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0569 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M057 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0570 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0571 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05711 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05712 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05719 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0572 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05721 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05722 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05729 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0573 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05731 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05732 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05739 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0574 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05741 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05742 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05749 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0575 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05751 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05752 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05759 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0576 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05761 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05762 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05769 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0577 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05771 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05772 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05779 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0579 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M058 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0580 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0581 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05811 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05812 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05819 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0582 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05821 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05822 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05829 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0583 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05831 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05832 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05839 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0584 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05841 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05842 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05849 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0585 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05851 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05852 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05859 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0586 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05861 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05862 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05869 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0587 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05871 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05872 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M05879 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0589 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M059 (M05) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M060 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0600 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0601 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06011 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06012 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06019 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0602 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06021 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06022 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06029 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0603 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06031 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06032 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06039 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0604 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06041 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06042 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06049 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0605 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06051 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06052 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06059 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0606 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06061 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06062 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06069 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0607 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06071 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06072 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06079 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0608 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0609 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M061 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0620 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0621 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06211 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06212 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06219 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0622 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06221 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06222 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06229 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0623 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06231 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06232 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06239 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0624 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06241 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06242 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06249 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0625 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06251 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06252 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06259 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0626 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06261 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06262 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06269 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0627 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06271 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06272 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06279 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0628 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0629 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M063 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0630 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0631 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06311 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06312 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06319 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0632 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06321 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06322 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06329 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0633 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06331 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06332 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06339 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0634 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06341 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06342 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06349 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0635 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06351 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06352 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06359 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0636 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06361 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06362 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06369 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0637 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06371 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06372 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06379 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0638 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0639 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M068 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0680 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0681 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06811 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06812 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06819 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0682 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06821 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06822 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06829 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0683 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06831 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06832 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06839 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0684 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06841 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06842 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06849 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0685 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06851 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06852 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06859 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0686 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06861 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06862 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06869 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0687 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06871 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06872 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M06879 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0688 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M0689 (M06) → 1 Delphi matches ✓ Rheumatoid_Arthritis: ICD10 M069 (M06) → 1 Delphi matches ✓ Psoriasis: ICD10 L400 (L40) → 1 Delphi matches ✓ Psoriasis: ICD10 L402 (L40) → 1 Delphi matches ✓ Psoriasis: ICD10 L403 (L40) → 1 Delphi matches ✓ Psoriasis: ICD10 L404 (L40) → 1 Delphi matches ✓ Psoriasis: ICD10 L408 (L40) → 1 Delphi matches ✓ Psoriasis: ICD10 L409 (L40) → 1 Delphi matches ✓ Ulcerative_Colitis: ICD10 K51 (K51) → 1 Delphi matches ✓ Ulcerative_Colitis: ICD10 K513 (K51) → 1 Delphi matches ✓ Ulcerative_Colitis: ICD10 K5130 (K51) → 1 Delphi matches ✓ Ulcerative_Colitis: ICD10 K51311 (K51) → 1 Delphi matches ✓ Ulcerative_Colitis: ICD10 K51312 (K51) → 1 Delphi matches ✓ Ulcerative_Colitis: ICD10 K51313 (K51) → 1 Delphi matches ✓ Ulcerative_Colitis: ICD10 K51314 (K51) → 1 Delphi matches ✓ Ulcerative_Colitis: ICD10 K51318 (K51) → 1 Delphi matches ✓ Ulcerative_Colitis: ICD10 K51319 (K51) → 1 Delphi matches ✓ Ulcerative_Colitis: ICD10 K514 (K51) → 1 Delphi matches ✓ Ulcerative_Colitis: ICD10 K5140 (K51) → 1 Delphi matches ✓ Ulcerative_Colitis: ICD10 K51411 (K51) → 1 Delphi matches ✓ Ulcerative_Colitis: ICD10 K51412 (K51) → 1 Delphi matches ✓ Ulcerative_Colitis: ICD10 K51413 (K51) → 1 Delphi matches ✓ Ulcerative_Colitis: ICD10 K51414 (K51) → 1 Delphi matches ✓ Ulcerative_Colitis: ICD10 K51418 (K51) → 1 Delphi matches ✓ Ulcerative_Colitis: ICD10 K51419 (K51) → 1 Delphi matches ✓ Ulcerative_Colitis: ICD10 K518 (K51) → 1 Delphi matches ✓ Ulcerative_Colitis: ICD10 K5180 (K51) → 1 Delphi matches ✓ Ulcerative_Colitis: ICD10 K51811 (K51) → 1 Delphi matches ✓ Ulcerative_Colitis: ICD10 K51812 (K51) → 1 Delphi matches ✓ Ulcerative_Colitis: ICD10 K51813 (K51) → 1 Delphi matches ✓ Ulcerative_Colitis: ICD10 K51814 (K51) → 1 Delphi matches ✓ Ulcerative_Colitis: ICD10 K51818 (K51) → 1 Delphi matches ✓ Ulcerative_Colitis: ICD10 K51819 (K51) → 1 Delphi matches ✓ Ulcerative_Colitis: ICD10 K519 (K51) → 1 Delphi matches ✓ Ulcerative_Colitis: ICD10 K5190 (K51) → 1 Delphi matches ✓ Ulcerative_Colitis: ICD10 K51911 (K51) → 1 Delphi matches ✓ Ulcerative_Colitis: ICD10 K51912 (K51) → 1 Delphi matches ✓ Ulcerative_Colitis: ICD10 K51913 (K51) → 1 Delphi matches ✓ Ulcerative_Colitis: ICD10 K51914 (K51) → 1 Delphi matches ✓ Ulcerative_Colitis: ICD10 K51918 (K51) → 1 Delphi matches ✓ Ulcerative_Colitis: ICD10 K51919 (K51) → 1 Delphi matches ✓ Crohns_Disease: ICD10 K50 (K50) → 1 Delphi matches ✓ Crohns_Disease: ICD10 K500 (K50) → 1 Delphi matches ✓ Crohns_Disease: ICD10 K5000 (K50) → 1 Delphi matches ✓ Crohns_Disease: ICD10 K50011 (K50) → 1 Delphi matches ✓ Crohns_Disease: ICD10 K50012 (K50) → 1 Delphi matches ✓ Crohns_Disease: ICD10 K50013 (K50) → 1 Delphi matches ✓ Crohns_Disease: ICD10 K50014 (K50) → 1 Delphi matches ✓ Crohns_Disease: ICD10 K50018 (K50) → 1 Delphi matches ✓ Crohns_Disease: ICD10 K50019 (K50) → 1 Delphi matches ✓ Crohns_Disease: ICD10 K501 (K50) → 1 Delphi matches ✓ Crohns_Disease: ICD10 K5010 (K50) → 1 Delphi matches ✓ Crohns_Disease: ICD10 K50111 (K50) → 1 Delphi matches ✓ Crohns_Disease: ICD10 K50112 (K50) → 1 Delphi matches ✓ Crohns_Disease: ICD10 K50113 (K50) → 1 Delphi matches ✓ Crohns_Disease: ICD10 K50114 (K50) → 1 Delphi matches ✓ Crohns_Disease: ICD10 K50118 (K50) → 1 Delphi matches ✓ Crohns_Disease: ICD10 K50119 (K50) → 1 Delphi matches ✓ Crohns_Disease: ICD10 K5080 (K50) → 1 Delphi matches ✓ Crohns_Disease: ICD10 K50811 (K50) → 1 Delphi matches ✓ Crohns_Disease: ICD10 K50812 (K50) → 1 Delphi matches ✓ Crohns_Disease: ICD10 K50813 (K50) → 1 Delphi matches ✓ Crohns_Disease: ICD10 K50814 (K50) → 1 Delphi matches ✓ Crohns_Disease: ICD10 K50818 (K50) → 1 Delphi matches ✓ Crohns_Disease: ICD10 K50819 (K50) → 1 Delphi matches ✓ Crohns_Disease: ICD10 K509 (K50) → 1 Delphi matches ✓ Crohns_Disease: ICD10 K5090 (K50) → 1 Delphi matches ✓ Crohns_Disease: ICD10 K50911 (K50) → 1 Delphi matches ✓ Crohns_Disease: ICD10 K50912 (K50) → 1 Delphi matches ✓ Crohns_Disease: ICD10 K50913 (K50) → 1 Delphi matches ✓ Crohns_Disease: ICD10 K50914 (K50) → 1 Delphi matches ✓ Crohns_Disease: ICD10 K50918 (K50) → 1 Delphi matches ✓ Crohns_Disease: ICD10 K50919 (K50) → 1 Delphi matches ✓ Asthma: ICD10 J45 (J45) → 1 Delphi matches ✓ Asthma: ICD10 J4520 (J45) → 1 Delphi matches ✓ Asthma: ICD10 J4530 (J45) → 1 Delphi matches ✓ Asthma: ICD10 J4540 (J45) → 1 Delphi matches ✓ Asthma: ICD10 J4550 (J45) → 1 Delphi matches ✓ Asthma: ICD10 J4590 (J45) → 1 Delphi matches ✓ Asthma: ICD10 J45909 (J45) → 1 Delphi matches ✓ Asthma: ICD10 J45991 (J45) → 1 Delphi matches ✓ Asthma: ICD10 J45998 (J45) → 1 Delphi matches ✓ Parkinsons: ICD10 G20 (G20) → 1 Delphi matches ✓ Parkinsons: ICD10 G214 (G21) → 1 Delphi matches ✓ Multiple_Sclerosis: ICD10 G35 (G35) → 1 Delphi matches ✓ Thyroid_Disorders: ICD10 E032 (E03) → 1 Delphi matches ✓ Thyroid_Disorders: ICD10 E039 (E03) → 1 Delphi matches ✓ Thyroid_Disorders: ICD10 E05 (E05) → 1 Delphi matches ✓ Thyroid_Disorders: ICD10 E051 (E05) → 1 Delphi matches ✓ Thyroid_Disorders: ICD10 E0510 (E05) → 1 Delphi matches ✓ Thyroid_Disorders: ICD10 E0511 (E05) → 1 Delphi matches ✓ Thyroid_Disorders: ICD10 E053 (E05) → 1 Delphi matches ✓ Thyroid_Disorders: ICD10 E0530 (E05) → 1 Delphi matches ✓ Thyroid_Disorders: ICD10 E0531 (E05) → 1 Delphi matches ✓ Thyroid_Disorders: ICD10 E054 (E05) → 1 Delphi matches ✓ Thyroid_Disorders: ICD10 E0540 (E05) → 1 Delphi matches ✓ Thyroid_Disorders: ICD10 E0541 (E05) → 1 Delphi matches ✓ Thyroid_Disorders: ICD10 E0580 (E05) → 1 Delphi matches ✓ Thyroid_Disorders: ICD10 E0581 (E05) → 1 Delphi matches ✓ Thyroid_Disorders: ICD10 E059 (E05) → 1 Delphi matches ✓ Thyroid_Disorders: ICD10 E0590 (E05) → 1 Delphi matches ✓ Thyroid_Disorders: ICD10 E0591 (E05) → 1 Delphi matches ✓ Thyroid_Disorders: ICD10 E890 (E89) → 1 Delphi matches ================================================================================ ✓ Extracted Delphi results: 82 ICD code matches across 28 diseases ================================================================================ Delphi results summary (1-to-many structure): Total Delphi ICD code matches: 82 Unique diseases: 28 Example (showing all ICD codes for first disease): Disease Delphi_ICD_code Delphi_t0 Delphi_name ASCVD I20 0.731225 I20 Angina pectoris ASCVD I21 0.650164 I21 Acute myocardial infarction ASCVD I22 0.928968 I22 Subsequent myocardial infarction ASCVD I23 0.896105 I23 Certain current complications following acute myocardial infarction ASCVD I24 0.849424 I24 Other acute ischaemic heart diseases ASCVD I25 0.823853 I25 Chronic ischaemic heart disease ASCVD I51 0.830445 I51 Complications and ill-defined descriptions of heart disease
Step 6: Load Aladynoulli t0 Predictions¶
Load our t0 predictions from washout_0yr_results.csv (predictions at enrollment, 0-year washout).
In [10]:
# Load Aladynoulli t0 predictions (0-year washout = predictions at enrollment)
aladynoulli_t0_path = Path('/Users/sarahurbut/aladynoulli2/pyScripts/dec_6_revision/new_notebooks/results/washout/pooled_retrospective/washout_0yr_results.csv')
if aladynoulli_t0_path.exists():
aladynoulli_t0 = pd.read_csv(aladynoulli_t0_path)
aladynoulli_t0 = aladynoulli_t0[['Disease', 'AUC']].copy()
aladynoulli_t0.columns = ['Disease', 'Aladynoulli_t0']
print(f"✓ Loaded Aladynoulli t0 predictions for {len(aladynoulli_t0)} diseases")
print(f"\nTop 10 diseases by AUC:")
print(aladynoulli_t0.nlargest(10, 'Aladynoulli_t0')[['Disease', 'Aladynoulli_t0']].to_string(index=False))
else:
print(f"⚠️ Aladynoulli results file not found: {aladynoulli_t0_path}")
aladynoulli_t0 = None
✓ Loaded Aladynoulli t0 predictions for 28 diseases
Top 10 diseases by AUC:
Disease Aladynoulli_t0
Crohns_Disease 0.896424
ASCVD 0.880921
Multiple_Sclerosis 0.839507
Prostate_Cancer 0.831185
Colorectal_Cancer 0.825333
Bladder_Cancer 0.824517
Ulcerative_Colitis 0.816088
Parkinsons 0.809074
Atrial_Fib 0.796554
Breast_Cancer 0.781816
Step 7: Compare Aladynoulli vs Delphi (t0 predictions)¶
Compare our t0 predictions (0-year washout) against Delphi's t0 predictions (no gap) using the 1-to-many structure.
In [144]:
# Merge and compare (1-to-many structure)
# Our t0 predictions vs Delphi's t0 predictions (no gap)
if aladynoulli_t0 is not None and len(delphi_results) > 0:
# Merge: our 1 prediction per disease with ALL Delphi ICD code matches
comparison = aladynoulli_t0.merge(
delphi_df[['Disease', 'Delphi_t0', 'Delphi_ICD_code', 'Delphi_name']],
on='Disease',
how='inner'
)
comparison['Advantage'] = comparison['Aladynoulli_t0'] - comparison['Delphi_t0']
comparison = comparison.sort_values(['Disease', 'Advantage'], ascending=[True, False])
print("="*80)
print("ALADYNOULLI vs DELPHI: t0 PREDICTIONS (1-to-Many Comparison)")
print("="*80)
print(f"\n{len(comparison)} comparisons ({len(set(comparison['Disease']))} diseases)")
print(f" Our model: 1 aggregated prediction per disease (0-year washout)")
print(f" Delphi: Multiple ICD code predictions per disease (no gap)")
# Count wins: our prediction beats at least one Delphi ICD code
wins_by_disease = comparison.groupby('Disease')['Advantage'].apply(lambda x: (x > 0).any())
n_wins = wins_by_disease.sum()
n_diseases = len(wins_by_disease)
print(f"\nAladynoulli wins (beats at least one Delphi ICD code): {n_wins}/{n_diseases} diseases ({n_wins/n_diseases*100:.1f}%)")
# Count how many Delphi ICD codes we beat per disease
beats_count = comparison.groupby('Disease')['Advantage'].apply(lambda x: (x > 0).sum())
total_delphi_codes = comparison.groupby('Disease').size()
print(f"\nMean advantage: {comparison['Advantage'].mean():.4f}")
print(f"Median advantage: {comparison['Advantage'].median():.4f}")
print("\n" + "-"*80)
print("Example: ASCVD (showing all Delphi ICD code comparisons):")
print("-"*80)
if 'ASCVD' in comparison['Disease'].values:
ascdv_comparison = comparison[comparison['Disease'] == 'ASCVD']
print(ascdv_comparison[['Disease', 'Aladynoulli_t0', 'Delphi_ICD_code', 'Delphi_t0', 'Advantage']].to_string(index=False))
# Save results (1-to-many structure)
output_dir = Path('/Users/sarahurbut/aladynoulli2/pyScripts/dec_6_revision/new_notebooks/results/comparisons/pooled_retrospective')
output_dir.mkdir(parents=True, exist_ok=True)
comparison_save = comparison.copy()
comparison_save['Win?'] = comparison_save['Advantage'].apply(lambda x: '✓' if x > 0 else '✗')
comparison_save = comparison_save.sort_values(['Disease', 'Advantage'], ascending=[True, False])
comparison_save.to_csv(output_dir / 'delphi_comparison_phecode_mapping_t0_1tomany.csv', index=False)
print(f"\n✓ Results saved to: {output_dir / 'delphi_comparison_phecode_mapping_t0_1tomany.csv'}")
else:
print("⚠️ Cannot create comparison without both Aladynoulli and Delphi results")
================================================================================ ALADYNOULLI vs DELPHI: t0 PREDICTIONS (1-to-Many Comparison) ================================================================================ 82 comparisons (28 diseases) Our model: 1 aggregated prediction per disease (0-year washout) Delphi: Multiple ICD code predictions per disease (no gap) Aladynoulli wins (beats at least one Delphi ICD code): 19/28 diseases (67.9%) Mean advantage: -0.0244 Median advantage: -0.0462 -------------------------------------------------------------------------------- Example: ASCVD (showing all Delphi ICD code comparisons): -------------------------------------------------------------------------------- Disease Aladynoulli_t0 Delphi_ICD_code Delphi_t0 Advantage ASCVD 0.880921 I21 0.650164 0.230757 ASCVD 0.880921 I20 0.731225 0.149696 ASCVD 0.880921 I25 0.823853 0.057068 ASCVD 0.880921 I51 0.830445 0.050476 ASCVD 0.880921 I24 0.849424 0.031497 ASCVD 0.880921 I23 0.896105 -0.015184 ASCVD 0.880921 I22 0.928968 -0.048047 ✓ Results saved to: /Users/sarahurbut/aladynoulli2/pyScripts/dec_6_revision/new_notebooks/results/comparisons/pooled_retrospective/delphi_comparison_phecode_mapping_t0_1tomany.csv
Step 9: Load Aladynoulli Median 1-Year Predictions¶
Load our median 1-year predictions (washout 0) from the age_offset results.
In [145]:
# Load Aladynoulli median 1-year predictions (washout 0)
aladynoulli_1yr_path = Path('/Users/sarahurbut/aladynoulli2/pyScripts/dec_6_revision/new_notebooks/results/age_offset/pooled_retrospective/medians_with_global0.csv')
if aladynoulli_1yr_path.exists():
aladynoulli_1yr = pd.read_csv(aladynoulli_1yr_path)
aladynoulli_1yr = aladynoulli_1yr[['Disease', 'Median_with_global0']].copy()
aladynoulli_1yr.columns = ['Disease', 'Aladynoulli_1yr']
print(f"✓ Loaded Aladynoulli median 1-year predictions for {len(aladynoulli_1yr)} diseases")
print(f"\nTop 10 diseases by AUC:")
print(aladynoulli_1yr.nlargest(10, 'Aladynoulli_1yr')[['Disease', 'Aladynoulli_1yr']].to_string(index=False))
else:
print(f"⚠️ Aladynoulli 1-year results file not found: {aladynoulli_1yr_path}")
aladynoulli_1yr = None
✓ Loaded Aladynoulli median 1-year predictions for 28 diseases
Top 10 diseases by AUC:
Disease Aladynoulli_1yr
Crohns_Disease 0.929851
Multiple_Sclerosis 0.901502
Bladder_Cancer 0.890949
ASCVD 0.879477
Breast_Cancer 0.867449
Colorectal_Cancer 0.848056
Prostate_Cancer 0.828300
Heart_Failure 0.810593
Ulcerative_Colitis 0.809364
Rheumatoid_Arthritis 0.801110
Step 10: Compare Aladynoulli vs Delphi (1-Year Predictions)¶
Compare our median 1-year predictions (washout 0) against Delphi's 1-year gap predictions using the 1-to-many structure.
In [146]:
# Merge and compare (1-to-many structure)
# Our median 1-year predictions (washout 0) vs Delphi's no gap (t0) predictions
if aladynoulli_1yr is not None and 'delphi_df' in locals() and len(delphi_df) > 0:
# Use Delphi t0 (no gap) predictions
delphi_t0 = delphi_df[delphi_df['Delphi_t0'].notna()].copy()
if len(delphi_t0) > 0:
# Merge: our 1 prediction per disease with ALL Delphi ICD code matches
comparison_1yr = aladynoulli_1yr.merge(
delphi_t0[['Disease', 'Delphi_t0', 'Delphi_ICD_code', 'Delphi_name']],
on='Disease',
how='inner'
)
# Rename for clarity
comparison_1yr = comparison_1yr.rename(columns={'Delphi_t0': 'Delphi_no_gap'})
comparison_1yr['Advantage'] = comparison_1yr['Aladynoulli_1yr'] - comparison_1yr['Delphi_no_gap']
comparison_1yr = comparison_1yr.sort_values(['Disease', 'Advantage'], ascending=[True, False])
print("="*80)
print("ALADYNOULLI vs DELPHI: 1-YEAR PREDICTIONS (1-to-Many Comparison)")
print("="*80)
print(f"\n{len(comparison_1yr)} comparisons ({len(set(comparison_1yr['Disease']))} diseases)")
print(f" Our model: 1 median aggregated prediction per disease (1-year, washout 0)")
print(f" Delphi: Multiple ICD code predictions per disease (no gap / t0)")
# Count wins: our prediction beats at least one Delphi ICD code
wins_by_disease = comparison_1yr.groupby('Disease')['Advantage'].apply(lambda x: (x > 0).any())
n_wins = wins_by_disease.sum()
n_diseases = len(wins_by_disease)
print(f"\nAladynoulli wins (beats at least one Delphi ICD code): {n_wins}/{n_diseases} diseases ({n_wins/n_diseases*100:.1f}%)")
print(f"\nMean advantage: {comparison_1yr['Advantage'].mean():.4f}")
print(f"Median advantage: {comparison_1yr['Advantage'].median():.4f}")
print("\n" + "-"*80)
print("Example: ASCVD (showing all Delphi ICD code comparisons):")
print("-"*80)
if 'ASCVD' in comparison_1yr['Disease'].values:
ascdv_comparison = comparison_1yr[comparison_1yr['Disease'] == 'ASCVD']
print(ascdv_comparison[['Disease', 'Aladynoulli_1yr', 'Delphi_ICD_code', 'Delphi_no_gap', 'Advantage']].to_string(index=False))
# Save results (1-to-many structure)
output_dir = Path('/Users/sarahurbut/aladynoulli2/pyScripts/dec_6_revision/new_notebooks/results/comparisons/pooled_retrospective')
output_dir.mkdir(parents=True, exist_ok=True)
comparison_1yr_save = comparison_1yr.copy()
comparison_1yr_save['Win?'] = comparison_1yr_save['Advantage'].apply(lambda x: '✓' if x > 0 else '✗')
comparison_1yr_save = comparison_1yr_save.sort_values(['Disease', 'Advantage'], ascending=[True, False])
comparison_1yr_save.to_csv(output_dir / 'delphi_comparison_phecode_mapping_1yr_1tomany.csv', index=False)
print(f"\n✓ Results saved to: {output_dir / 'delphi_comparison_phecode_mapping_1yr_1tomany.csv'}")
else:
print("⚠️ No Delphi t0 (no gap) predictions found in delphi_df")
else:
print("⚠️ Cannot create comparison without both Aladynoulli 1-year and Delphi results")
================================================================================ ALADYNOULLI vs DELPHI: 1-YEAR PREDICTIONS (1-to-Many Comparison) ================================================================================ 82 comparisons (28 diseases) Our model: 1 median aggregated prediction per disease (1-year, washout 0) Delphi: Multiple ICD code predictions per disease (no gap / t0) Aladynoulli wins (beats at least one Delphi ICD code): 19/28 diseases (67.9%) Mean advantage: 0.0120 Median advantage: -0.0122 -------------------------------------------------------------------------------- Example: ASCVD (showing all Delphi ICD code comparisons): -------------------------------------------------------------------------------- Disease Aladynoulli_1yr Delphi_ICD_code Delphi_no_gap Advantage ASCVD 0.879477 I21 0.650164 0.229313 ASCVD 0.879477 I20 0.731225 0.148252 ASCVD 0.879477 I25 0.823853 0.055624 ASCVD 0.879477 I51 0.830445 0.049032 ASCVD 0.879477 I24 0.849424 0.030053 ASCVD 0.879477 I23 0.896105 -0.016628 ASCVD 0.879477 I22 0.928968 -0.049491 ✓ Results saved to: /Users/sarahurbut/aladynoulli2/pyScripts/dec_6_revision/new_notebooks/results/comparisons/pooled_retrospective/delphi_comparison_phecode_mapping_1yr_1tomany.csv
Step 11: Visualization - 1-Year Predictions Comparison¶
Create a scatter plot showing our median 1-year aggregated prediction (x-axis) vs all matching Delphi 1-year gap ICD code predictions (y-axis), color-coded by disease.
In [152]:
# Create 1-to-many visualization for 1-year predictions: Our single prediction vs all Delphi ICD codes, color-coded by disease
# UPDATED: Delphi on x-axis, Aladynoulli on y-axis
if 'comparison_1yr' in locals() and len(comparison_1yr) > 0:
# Set up the plot style
sns.set_style("whitegrid")
plt.rcParams['figure.figsize'] = (16, 12)
plt.rcParams['font.size'] = 9
# Get unique diseases and assign colors (use same colors as t0 plot for consistency)
unique_diseases = sorted(comparison_1yr['Disease'].unique())
n_diseases = len(unique_diseases)
# Use a colormap with enough distinct colors
colors = plt.cm.tab20(np.linspace(0, 1, 20))
if n_diseases > 20:
# Extend with another colormap if needed
colors2 = plt.cm.Set3(np.linspace(0, 1, 12))
colors = np.vstack([colors, colors2])
disease_colors = {disease: colors[i % len(colors)] for i, disease in enumerate(unique_diseases)}
# Create figure
fig, ax = plt.subplots(figsize=(16, 12))
# Plot diagonal line (y=x) for reference
min_val = min(comparison_1yr['Aladynoulli_1yr'].min(), comparison_1yr['Delphi_no_gap'].min())
max_val = max(comparison_1yr['Aladynoulli_1yr'].max(), comparison_1yr['Delphi_no_gap'].max())
ax.plot([min_val, max_val], [min_val, max_val], 'k--', alpha=0.3, linewidth=1, label='y=x (equal performance)')
# Calculate Delphi variability statistics
delphi_variability = []
# For each disease, plot lines connecting our prediction to all Delphi points
for disease in unique_diseases:
disease_data = comparison_1yr[comparison_1yr['Disease'] == disease]
our_auc = disease_data['Aladynoulli_1yr'].iloc[0] # Same for all rows
delphi_aucs = disease_data['Delphi_no_gap'].values
color = disease_colors[disease]
# Calculate variability metrics
if len(delphi_aucs) > 1:
delphi_range = delphi_aucs.max() - delphi_aucs.min()
delphi_std = delphi_aucs.std()
delphi_variability.append({
'Disease': disease,
'Range': delphi_range,
'Std': delphi_std,
'Min': delphi_aucs.min(),
'Max': delphi_aucs.max(),
'N': len(delphi_aucs)
})
# Draw shaded region showing Delphi range (behind everything) - HORIZONTAL BAND (Delphi on x-axis)
ax.fill_between([delphi_aucs.min(), delphi_aucs.max()],
[our_auc - 0.005, our_auc - 0.005],
[our_auc + 0.005, our_auc + 0.005],
color=color, alpha=0.15, zorder=0)
# Draw lines from our point to each Delphi point - HORIZONTAL LINES (Delphi on x-axis)
for delphi_auc in delphi_aucs:
ax.plot([delphi_auc, our_auc], [our_auc, our_auc],
color=color, alpha=0.3, linewidth=0.8, zorder=1)
# Plot all Delphi points for this disease - DELPHI ON X-AXIS, ALA ON Y-AXIS
ax.scatter(delphi_aucs, [our_auc] * len(delphi_aucs),
s=80, c=[color], marker='o',
edgecolors='black', linewidths=0.8,
alpha=0.7, zorder=2)
# Add text annotation showing range for diseases with multiple Delphi codes - SWAPPED POSITION
if len(delphi_aucs) > 1:
range_text = f"Δ={delphi_range:.3f}"
ax.text((delphi_aucs.min() + delphi_aucs.max()) / 2, our_auc + 0.01,
range_text, fontsize=7, color=color,
weight='bold', alpha=0.8, zorder=4,
bbox=dict(boxstyle='round,pad=0.3', facecolor='white',
edgecolor=color, alpha=0.7, linewidth=1))
# Labels and title - SWAPPED AXES
ax.set_xlabel('Delphi AUC (no gap / t0)', fontsize=12, fontweight='bold')
ax.set_ylabel('Aladynoulli AUC (1-year, washout 0)', fontsize=12, fontweight='bold')
ax.set_title('Aladynoulli vs Delphi: 1-Year Predictions (1-to-Many Comparison)\n(Our median 1-year aggregated prediction vs all matching Delphi ICD codes, no gap)',
fontsize=14, fontweight='bold', pad=20)
# Add grid
ax.grid(True, alpha=0.3, linestyle='--')
# Set equal aspect ratio and limits
ax.set_aspect('equal', adjustable='box')
margin = 0.05
ax.set_xlim([min_val - margin, max_val + margin])
ax.set_ylim([min_val - margin, max_val + margin])
# Add legend (outside plot, showing all diseases)
legend_elements = []
for i, disease in enumerate(unique_diseases): # Show all diseases
color = disease_colors[disease]
n_delphi = len(comparison_1yr[comparison_1yr['Disease'] == disease])
legend_elements.append(
plt.Line2D([0], [0], marker='s', color='w', markerfacecolor=color,
markersize=8, markeredgecolor='black', markeredgewidth=1,
label=f'{disease} ({n_delphi})', linestyle='None')
)
# Place legend outside plot
ax.legend(handles=legend_elements, loc='center left', bbox_to_anchor=(1.02, 0.5),
fontsize=8, frameon=True, fancybox=True, shadow=True)
# Add text annotation with summary stats
wins_by_disease = comparison_1yr.groupby('Disease')['Advantage'].apply(lambda x: (x > 0).any())
n_wins = wins_by_disease.sum()
n_diseases_total = len(wins_by_disease)
# Calculate Delphi variability summary
if len(delphi_variability) > 0:
variability_df = pd.DataFrame(delphi_variability)
mean_range = variability_df['Range'].mean()
mean_std = variability_df['Std'].mean()
max_range = variability_df['Range'].max()
max_range_disease = variability_df.loc[variability_df['Range'].idxmax(), 'Disease']
stats_text = (f'Aladynoulli beats ≥1 Delphi ICD code: {n_wins}/{n_diseases_total} diseases ({n_wins/n_diseases_total*100:.1f}%)\n'
f'Delphi variability: Mean range = {mean_range:.3f}, Mean std = {mean_std:.3f}\n'
f'Max Delphi range: {max_range:.3f} ({max_range_disease})')
else:
stats_text = f'Aladynoulli beats ≥1 Delphi ICD code: {n_wins}/{n_diseases_total} diseases ({n_wins/n_diseases_total*100:.1f}%)'
ax.text(0.02, 0.98, stats_text, transform=ax.transAxes,
fontsize=9, verticalalignment='top',
bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.8))
# Print variability summary
if len(delphi_variability) > 0:
print("\n" + "="*80)
print("DELPHI VARIABILITY SUMMARY (1-YEAR PREDICTIONS):")
print("="*80)
variability_df = pd.DataFrame(delphi_variability)
variability_df = variability_df.sort_values('Range', ascending=False)
print(f"\nMean range across diseases: {variability_df['Range'].mean():.4f}")
print(f"Mean std across diseases: {variability_df['Std'].mean():.4f}")
print(f"\nTop 10 diseases by Delphi variability (range):")
print(variability_df[['Disease', 'N', 'Range', 'Std', 'Min', 'Max']].head(10).to_string(index=False))
plt.tight_layout()
# Save figure
figures_dir = Path('/Users/sarahurbut/aladynoulli2/pyScripts/dec_6_revision/new_notebooks/figures')
figures_dir.mkdir(parents=True, exist_ok=True)
fig_path = figures_dir / 'delphi_comparison_phecode_mapping_1yr_1tomany.png'
plt.savefig(fig_path, dpi=300, bbox_inches='tight')
print(f"✓ Saved figure to: {fig_path}")
plt.show()
else:
print("⚠️ Cannot create visualization without comparison_1yr data")
================================================================================
DELPHI VARIABILITY SUMMARY (1-YEAR PREDICTIONS):
================================================================================
Mean range across diseases: 0.1847
Mean std across diseases: 0.0720
Top 10 diseases by Delphi variability (range):
Disease N Range Std Min Max
Pneumonia 11 0.396137 0.100356 0.492292 0.888429
All_Cancers 11 0.385372 0.122711 0.483427 0.868800
Colorectal_Cancer 6 0.321541 0.103193 0.483427 0.804968
Secondary_Cancer 4 0.299675 0.124712 0.569124 0.868800
Thyroid_Disorders 3 0.299586 0.130603 0.616972 0.916558
Parkinsons 2 0.285136 0.142568 0.610783 0.895919
ASCVD 7 0.278804 0.088845 0.650164 0.928968
Rheumatoid_Arthritis 2 0.226658 0.113329 0.690491 0.917149
Depression 2 0.167130 0.083565 0.698921 0.866051
Bipolar_Disorder 3 0.151161 0.062768 0.698921 0.850082
✓ Saved figure to: /Users/sarahurbut/aladynoulli2/pyScripts/dec_6_revision/new_notebooks/figures/delphi_comparison_phecode_mapping_1yr_1tomany.png
Step 12: Combined Comparison File
Create a single file combining both t0 and 1-year predictions with Delphi t0.
In [148]:
# Combine both comparisons into a single file
if 'comparison' in locals() and 'comparison_1yr' in locals():
# Merge on Disease and Delphi_ICD_code
combined = comparison[['Disease', 'Delphi_ICD_code', 'Delphi_name', 'Aladynoulli_t0', 'Delphi_t0']].merge(
comparison_1yr[['Disease', 'Delphi_ICD_code', 'Aladynoulli_1yr']],
on=['Disease', 'Delphi_ICD_code'],
how='outer' # Use outer to keep all rows from both
)
# Calculate advantages for both
combined['Advantage_t0'] = combined['Aladynoulli_t0'] - combined['Delphi_t0']
combined['Advantage_1yr'] = combined['Aladynoulli_1yr'] - combined['Delphi_t0']
# Add win indicators
combined['Win_t0?'] = combined['Advantage_t0'].apply(lambda x: '✓' if pd.notna(x) and x > 0 else '✗' if pd.notna(x) else '')
combined['Win_1yr?'] = combined['Advantage_1yr'].apply(lambda x: '✓' if pd.notna(x) and x > 0 else '✗' if pd.notna(x) else '')
# Sort by Disease and then by Advantage_1yr (descending)
combined = combined.sort_values(['Disease', 'Advantage_1yr'], ascending=[True, False], na_position='last')
# Reorder columns
combined = combined[['Disease', 'Delphi_ICD_code', 'Delphi_name',
'Aladynoulli_t0', 'Aladynoulli_1yr', 'Delphi_t0',
'Advantage_t0', 'Advantage_1yr', 'Win_t0?', 'Win_1yr?']]
print("="*80)
print("COMBINED COMPARISON: Aladynoulli t0 & 1-year vs Delphi t0")
print("="*80)
print(f"\n{len(combined)} comparisons ({len(set(combined['Disease']))} diseases)")
print(f" Aladynoulli t0: {combined['Aladynoulli_t0'].notna().sum()} comparisons")
print(f" Aladynoulli 1-year: {combined['Aladynoulli_1yr'].notna().sum()} comparisons")
print(f" Delphi t0: {combined['Delphi_t0'].notna().sum()} comparisons")
# Count wins
wins_t0 = (combined['Advantage_t0'] > 0).sum()
wins_1yr = (combined['Advantage_1yr'] > 0).sum()
total_t0 = combined['Advantage_t0'].notna().sum()
total_1yr = combined['Advantage_1yr'].notna().sum()
print(f"\nAladynoulli wins (t0 vs Delphi t0): {wins_t0}/{total_t0} ({wins_t0/total_t0*100:.1f}%)")
print(f"Aladynoulli wins (1-year vs Delphi t0): {wins_1yr}/{total_1yr} ({wins_1yr/total_1yr*100:.1f}%)")
print("\n" + "-"*80)
print("Example: ASCVD (showing all Delphi ICD code comparisons):")
print("-"*80)
if 'ASCVD' in combined['Disease'].values:
ascdv_combined = combined[combined['Disease'] == 'ASCVD']
print(ascdv_combined[['Disease', 'Delphi_ICD_code', 'Aladynoulli_t0', 'Aladynoulli_1yr',
'Delphi_t0', 'Advantage_t0', 'Advantage_1yr', 'Win_t0?', 'Win_1yr?']].to_string(index=False))
# Save combined results
output_dir = Path('/Users/sarahurbut/aladynoulli2/pyScripts/dec_6_revision/new_notebooks/results/comparisons/pooled_retrospective')
output_dir.mkdir(parents=True, exist_ok=True)
combined.to_csv(output_dir / 'delphi_comparison_phecode_mapping_combined_t0_1yr.csv', index=False)
print(f"\n✓ Combined results saved to: {output_dir / 'delphi_comparison_phecode_mapping_combined_t0_1yr.csv'}")
else:
print("⚠️ Cannot create combined file without both comparison and comparison_1yr data")
================================================================================ COMBINED COMPARISON: Aladynoulli t0 & 1-year vs Delphi t0 ================================================================================ 82 comparisons (28 diseases) Aladynoulli t0: 82 comparisons Aladynoulli 1-year: 82 comparisons Delphi t0: 82 comparisons Aladynoulli wins (t0 vs Delphi t0): 36/82 (43.9%) Aladynoulli wins (1-year vs Delphi t0): 37/82 (45.1%) -------------------------------------------------------------------------------- Example: ASCVD (showing all Delphi ICD code comparisons): -------------------------------------------------------------------------------- Disease Delphi_ICD_code Aladynoulli_t0 Aladynoulli_1yr Delphi_t0 Advantage_t0 Advantage_1yr Win_t0? Win_1yr? ASCVD I21 0.880921 0.879477 0.650164 0.230757 0.229313 ✓ ✓ ASCVD I20 0.880921 0.879477 0.731225 0.149696 0.148252 ✓ ✓ ASCVD I25 0.880921 0.879477 0.823853 0.057068 0.055624 ✓ ✓ ASCVD I51 0.880921 0.879477 0.830445 0.050476 0.049032 ✓ ✓ ASCVD I24 0.880921 0.879477 0.849424 0.031497 0.030053 ✓ ✓ ASCVD I23 0.880921 0.879477 0.896105 -0.015184 -0.016628 ✗ ✗ ASCVD I22 0.880921 0.879477 0.928968 -0.048047 -0.049491 ✗ ✗ ✓ Combined results saved to: /Users/sarahurbut/aladynoulli2/pyScripts/dec_6_revision/new_notebooks/results/comparisons/pooled_retrospective/delphi_comparison_phecode_mapping_combined_t0_1yr.csv
Alternative Comparison: Simple String Matching (Manual Mapping)¶
This cell recreates the old manual mapping approach from compare_delphi_1yr_import.py using simple string matching to map Delphi ICD codes to our diseases. This provides a comparison to the Phecode-based mapping approach above.
Method:
- Uses simple string matching (ICD code prefix or disease name substring)
- Averages Delphi's AUCs across all matching ICD codes per disease
- Compares Aladynoulli's median 1-year predictions (washout 0) vs Delphi's 0-gap (t0) predictions
In [149]:
# Manual dictionary mapping approach (recreating compare_delphi_1yr_import.py logic)
# Uses the hardcoded disease-to-ICD mapping from the original script
# Get our disease names from Aladynoulli results
if aladynoulli_1yr is not None:
our_disease_names = aladynoulli_1yr['Disease'].unique().tolist()
# Load Delphi supplementary table if not already loaded
if 'delphi_supp' not in locals():
delphi_supp_path = Path('/Users/sarahurbut/Downloads/41586_2025_9529_MOESM3_ESM.csv')
if delphi_supp_path.exists():
delphi_supp = pd.read_csv(delphi_supp_path)
print(f"✓ Loaded Delphi supplementary table: {len(delphi_supp)} rows")
else:
print(f"⚠️ Delphi supplementary table not found: {delphi_supp_path}")
delphi_supp = None
if delphi_supp is not None:
# Manual disease category to ICD-10 code mappings (from compare_delphi_1yr_import.py)
disease_icd_mapping = {
'ASCVD': ['I21', 'I25'], # Myocardial infarction, Coronary atherosclerosis
'Diabetes': ['E11'], # Type 2 diabetes
'Atrial_Fib': ['I48'], # Atrial fibrillation
'CKD': ['N18'], # Chronic renal failure
'All_Cancers': ['C18', 'C50', 'D07'], # Colon, Breast, Prostate
'Stroke': ['I63'], # Cerebral infarction
'Heart_Failure': ['I50'], # Heart failure
'Pneumonia': ['J18'], # Pneumonia
'COPD': ['J44'], # Chronic obstructive pulmonary disease
'Osteoporosis': ['M81'], # Osteoporosis
'Anemia': ['D50'], # Iron deficiency anemia
'Colorectal_Cancer': ['C18'], # Colon cancer
'Breast_Cancer': ['C50'], # Breast cancer
'Prostate_Cancer': ['C61'], # Prostate cancer
'Lung_Cancer': ['C34'], # Lung cancer
'Bladder_Cancer': ['C67'], # Bladder cancer
'Secondary_Cancer': ['C79'], # Secondary malignant neoplasm
'Depression': ['F32', 'F33'], # Depressive disorders
'Anxiety': ['F41'], # Anxiety disorders
'Bipolar_Disorder': ['F31'], # Bipolar disorder
'Rheumatoid_Arthritis': ['M05', 'M06'], # Rheumatoid arthritis
'Psoriasis': ['L40'], # Psoriasis
'Ulcerative_Colitis': ['K51'], # Ulcerative colitis
'Crohns_Disease': ['K50'], # Crohn's disease
'Asthma': ['J45'], # Asthma
'Parkinsons': ['G20'], # Parkinson's disease
'Multiple_Sclerosis': ['G35'], # Multiple sclerosis
'Thyroid_Disorders': ['E03'] # Hypothyroidism
}
# Extract Delphi AUCs for each disease category
disease_icd_mapping_simple = {} # Track which ICD codes map to which diseases
# Identify Delphi column names
name_col = None
auc_0gap_female_col = None
auc_0gap_male_col = None
for col in delphi_supp.columns:
col_lower = col.lower()
if 'name' in col_lower and name_col is None:
name_col = col
if 'auc' in col_lower and 'female' in col_lower and 'no gap' in col_lower:
auc_0gap_female_col = col
if 'auc' in col_lower and 'male' in col_lower and 'no gap' in col_lower:
auc_0gap_male_col = col
if name_col:
for disease_name, icd_codes in disease_icd_mapping.items():
matching_rows = []
for icd_code in icd_codes:
# Find ICD-10 codes that start with the pattern
matches = delphi_supp[delphi_supp[name_col].str.contains(f'^{icd_code}', regex=True, na=False)]
if len(matches) > 0:
matching_rows.append(matches)
if len(matching_rows) > 0:
# Combine all matching rows
combined = pd.concat(matching_rows).drop_duplicates()
# Store ICD code mappings for this disease
disease_icd_mapping_simple[disease_name] = []
for idx, row in combined.iterrows():
name_val = str(row[name_col])
# Extract ICD code
import re
icd_match = re.match(r'^([A-Z]\d{2})', name_val)
if icd_match:
icd_code = icd_match.group(1)
else:
continue
# Get AUCs for 0-gap (t0)
female_auc = row[auc_0gap_female_col] if auc_0gap_female_col in row.index and pd.notna(row[auc_0gap_female_col]) else None
male_auc = row[auc_0gap_male_col] if auc_0gap_male_col in row.index and pd.notna(row[auc_0gap_male_col]) else None
# Average if both available
if female_auc is not None and male_auc is not None:
avg_auc = (female_auc + male_auc) / 2
elif female_auc is not None:
avg_auc = female_auc
elif male_auc is not None:
avg_auc = male_auc
else:
continue
disease_icd_mapping_simple[disease_name].append({
'ICD_code': icd_code,
'Delphi_name': name_val,
'Delphi_0gap': avg_auc
})
# Create comparison: average Delphi AUCs per disease
simple_comparison_results = []
for disease in our_disease_names:
if disease in disease_icd_mapping_simple and len(disease_icd_mapping_simple[disease]) > 0:
# Get Aladynoulli AUC
ala_auc = aladynoulli_1yr[aladynoulli_1yr['Disease'] == disease]['Aladynoulli_1yr'].iloc[0]
# Average Delphi AUCs across all matching ICD codes
delphi_aucs = [item['Delphi_0gap'] for item in disease_icd_mapping_simple[disease]]
avg_delphi_auc = np.mean(delphi_aucs)
# Get ICD codes for display
icd_codes = [item['ICD_code'] for item in disease_icd_mapping_simple[disease]]
simple_comparison_results.append({
'Disease': disease,
'Aladynoulli_1yr': ala_auc,
'Delphi_0gap_avg': avg_delphi_auc,
'N_ICD_codes': len(icd_codes),
'ICD_codes': ', '.join(icd_codes),
'Advantage': ala_auc - avg_delphi_auc
})
simple_comparison_df = pd.DataFrame(simple_comparison_results)
if len(simple_comparison_df) > 0:
simple_comparison_df = simple_comparison_df.sort_values('Advantage', ascending=False)
print("="*80)
print("MANUAL DICTIONARY MAPPING COMPARISON")
print("="*80)
print(f"\nMapped {len(simple_comparison_df)} diseases using manual dictionary mapping")
print(f"\nICD Code Mapping (showing which ICD codes map to each disease):")
print("-"*80)
for disease, items in sorted(disease_icd_mapping_simple.items()):
icd_codes = [item['ICD_code'] for item in items]
print(f" {disease}: {', '.join(icd_codes)} ({len(icd_codes)} codes)")
print("\n" + "="*80)
print("COMPARISON: Aladynoulli Median 1-Year (Washout 0) vs Delphi 0-Gap (Averaged)")
print("="*80)
wins = simple_comparison_df[simple_comparison_df['Advantage'] > 0]
print(f"\nAladynoulli wins: {len(wins)}/{len(simple_comparison_df)} diseases ({len(wins)/len(simple_comparison_df)*100:.1f}%)")
print(f"Mean advantage: {simple_comparison_df['Advantage'].mean():.4f}")
print(f"Median advantage: {simple_comparison_df['Advantage'].median():.4f}")
print("\nTop 10 diseases by advantage:")
print(simple_comparison_df.head(10)[['Disease', 'Aladynoulli_1yr', 'Delphi_0gap_avg', 'N_ICD_codes', 'Advantage']].to_string(index=False))
print("\nBottom 10 diseases (Delphi wins):")
print(simple_comparison_df.tail(10)[['Disease', 'Aladynoulli_1yr', 'Delphi_0gap_avg', 'N_ICD_codes', 'Advantage']].to_string(index=False))
# Save results
output_dir = Path('/Users/sarahurbut/aladynoulli2/pyScripts/dec_6_revision/new_notebooks/results/comparisons/pooled_retrospective')
output_dir.mkdir(parents=True, exist_ok=True)
simple_comparison_df.to_csv(output_dir / 'delphi_comparison_simple_mapping_1yr.csv', index=False)
print(f"\n✓ Results saved to: {output_dir / 'delphi_comparison_simple_mapping_1yr.csv'}")
# Create visualization
import matplotlib.pyplot as plt
import seaborn as sns
fig, ax = plt.subplots(figsize=(12, 10))
scatter = ax.scatter(simple_comparison_df['Delphi_0gap_avg'],
simple_comparison_df['Aladynoulli_1yr'],
s=100, alpha=0.7, c=simple_comparison_df['Advantage'],
cmap='RdYlGn', edgecolors='black', linewidth=1.5, vmin=-0.2, vmax=0.2)
for idx, row in simple_comparison_df.iterrows():
ax.annotate(row['Disease'],
(row['Delphi_0gap_avg'], row['Aladynoulli_1yr']),
fontsize=8, alpha=0.8)
max_val = max(simple_comparison_df['Delphi_0gap_avg'].max(), simple_comparison_df['Aladynoulli_1yr'].max())
min_val = min(simple_comparison_df['Delphi_0gap_avg'].min(), simple_comparison_df['Aladynoulli_1yr'].min())
ax.plot([min_val, max_val], [min_val, max_val], 'k--', alpha=0.4, linewidth=2, label='Equal performance')
ax.set_xlabel('Delphi AUC (0-gap, averaged across ICD codes)', fontsize=12, fontweight='bold')
ax.set_ylabel('Aladynoulli AUC (Median 1-year, washout 0)', fontsize=12, fontweight='bold')
ax.set_title(f'Aladynoulli vs Delphi: Manual Dictionary Mapping Comparison\n'
f'Aladynoulli wins: {len(wins)}/{len(simple_comparison_df)} ({len(wins)/len(simple_comparison_df)*100:.1f}%) | '
f'Mean advantage: {simple_comparison_df["Advantage"].mean():+.4f}',
fontsize=13, fontweight='bold', pad=20)
ax.legend(fontsize=9)
ax.grid(alpha=0.3)
plt.colorbar(scatter, ax=ax, label='Advantage', shrink=0.8)
plt.tight_layout()
# Save figure
fig_path = Path('/Users/sarahurbut/aladynoulli2/pyScripts/dec_6_revision/new_notebooks/figures/delphi_comparison_simple_mapping_1yr.png')
fig_path.parent.mkdir(parents=True, exist_ok=True)
plt.savefig(fig_path, dpi=300, bbox_inches='tight')
print(f"\n✓ Figure saved to: {fig_path}")
plt.show()
else:
print("⚠️ No diseases matched using manual dictionary mapping")
else:
print("⚠️ Cannot perform simple mapping comparison without Delphi supplementary table")
else:
print("⚠️ Cannot perform simple mapping comparison without Aladynoulli results")
================================================================================
MANUAL DICTIONARY MAPPING COMPARISON
================================================================================
Mapped 28 diseases using manual dictionary mapping
ICD Code Mapping (showing which ICD codes map to each disease):
--------------------------------------------------------------------------------
ASCVD: I21, I25 (2 codes)
All_Cancers: C18, C50, D07 (3 codes)
Anemia: D50 (1 codes)
Anxiety: F41 (1 codes)
Asthma: J45 (1 codes)
Atrial_Fib: I48 (1 codes)
Bipolar_Disorder: F31 (1 codes)
Bladder_Cancer: C67 (1 codes)
Breast_Cancer: C50 (1 codes)
CKD: N18 (1 codes)
COPD: J44 (1 codes)
Colorectal_Cancer: C18 (1 codes)
Crohns_Disease: K50 (1 codes)
Depression: F32, F33 (2 codes)
Diabetes: E11 (1 codes)
Heart_Failure: I50 (1 codes)
Lung_Cancer: C34 (1 codes)
Multiple_Sclerosis: G35 (1 codes)
Osteoporosis: M81 (1 codes)
Parkinsons: G20 (1 codes)
Pneumonia: J18 (1 codes)
Prostate_Cancer: C61 (1 codes)
Psoriasis: L40 (1 codes)
Rheumatoid_Arthritis: M05, M06 (2 codes)
Secondary_Cancer: C79 (1 codes)
Stroke: I63 (1 codes)
Thyroid_Disorders: E03 (1 codes)
Ulcerative_Colitis: K51 (1 codes)
================================================================================
COMPARISON: Aladynoulli Median 1-Year (Washout 0) vs Delphi 0-Gap (Averaged)
================================================================================
Aladynoulli wins: 15/28 diseases (53.6%)
Mean advantage: 0.0344
Median advantage: 0.0379
Top 10 diseases by advantage:
Disease Aladynoulli_1yr Delphi_0gap_avg N_ICD_codes Advantage
Multiple_Sclerosis 0.901502 0.654503 1 0.247000
Parkinsons 0.795831 0.610783 1 0.185048
Breast_Cancer 0.867449 0.698512 1 0.168937
Prostate_Cancer 0.828300 0.663622 1 0.164678
Bladder_Cancer 0.890949 0.747730 1 0.143219
ASCVD 0.879477 0.737008 2 0.142468
Atrial_Fib 0.800828 0.672077 1 0.128750
Crohns_Disease 0.929851 0.814220 1 0.115630
Secondary_Cancer 0.682989 0.569124 1 0.113865
Asthma 0.701957 0.614408 1 0.087549
Bottom 10 diseases (Delphi wins):
Disease Aladynoulli_1yr Delphi_0gap_avg N_ICD_codes Advantage
Heart_Failure 0.810593 0.850223 1 -0.039629
CKD 0.759655 0.803340 1 -0.043684
Diabetes 0.787448 0.833574 1 -0.046125
Lung_Cancer 0.783587 0.845299 1 -0.061713
Anemia 0.690466 0.752748 1 -0.062282
Stroke 0.674359 0.754460 1 -0.080102
COPD 0.737509 0.820437 1 -0.082928
Bipolar_Disorder 0.757939 0.850082 1 -0.092143
Anxiety 0.639015 0.764152 1 -0.125136
Depression 0.646617 0.782486 2 -0.135869
✓ Results saved to: /Users/sarahurbut/aladynoulli2/pyScripts/dec_6_revision/new_notebooks/results/comparisons/pooled_retrospective/delphi_comparison_simple_mapping_1yr.csv
✓ Figure saved to: /Users/sarahurbut/aladynoulli2/pyScripts/dec_6_revision/new_notebooks/figures/delphi_comparison_simple_mapping_1yr.png
