EOCS-LTD/elixhauser-icd10-mapping

AHRQ Elixhauser ICD-10-CM to Comorbidity Mapping

4,543 ICD-10-CM diagnosis codes mapped to 38 Elixhauser comorbidity categories with van Walraven mortality weights.

This dataset provides the complete ICD-10-CM to Elixhauser comorbidity crosswalk from the AHRQ Healthcare Cost and Utilization Project (HCUP) Elixhauser Comorbidity Software Refined for ICD-10-CM. It is published as a clean, versioned, machine-readable CSV for integration into clinical analytics, risk adjustment, and AI/ML pipelines.

Files

elixhauser_icd10_mapping.csv

The primary mapping table. Each row is one ICD-10-CM diagnosis code with binary flags (0/1) for each of the 38 Elixhauser comorbidity categories.

Column	Description
icd10cm_code	ICD-10-CM diagnosis code (no periods)
icd10cm_description	Clinical description of the code
comorbidity_count	Number of comorbidity categories this code maps to
AIDS through WGHTLOSS	39 binary columns (0/1) for each comorbidity category

Note: Cerebrovascular disease is split into two mapping columns: CBVD_POA (present on admission) and CBVD_SQLA (sequela). These are combined into a single CBVD comorbidity measure in the scoring.

comorbidity_measures.csv

Reference table for the 38 Elixhauser comorbidity categories.

Column	Description
abbreviation	AHRQ abbreviation (e.g., HF, DIAB_UNCX)
description	Full clinical description
requires_poa	Whether present-on-admission indicators are used for assignment (Yes/No)
van_walraven_mortality_weight	Van Walraven point weight for in-hospital mortality prediction

Quick Start

Load with Pandas

import pandas as pd

# Load the mapping
mapping = pd.read_csv("elixhauser_icd10_mapping.csv")

# Find all comorbidities for a patient's diagnosis codes
patient_dx = ["I509", "E119", "I10", "N183", "E6601"]
patient_flags = mapping[mapping["icd10cm_code"].isin(patient_dx)]

# Aggregate: any code flagging a comorbidity = patient has it
comorbidity_cols = mapping.columns[3:]  # skip code, description, count
patient_comorbidities = patient_flags[comorbidity_cols].max()
flagged = patient_comorbidities[patient_comorbidities == 1].index.tolist()
print(f"Flagged comorbidities: {flagged}")

Load with Hugging Face Datasets

from datasets import load_dataset

dataset = load_dataset("EOCS/elixhauser-icd10-mapping")
df = dataset["train"].to_pandas()

Use with the elixhauser-ahrq Python Package

For production use with van Walraven scoring and risk tier assignment:

pip install elixhauser-ahrq

from elixhauser_ahrq import ElixhauserMapper, ElixhauserScorer

mapper = ElixhauserMapper()
flags = mapper.map_patient_diagnoses(["I509", "E119", "I10"])

scorer = ElixhauserScorer(index_type="mortality")
score = scorer.calculate_score(flags)
print(f"van Walraven Score: {score}")

Use Cases

• Risk adjustment for episode-based care: Stratify surgical and medical episode populations by comorbidity burden
• Feature engineering for clinical ML models: Use comorbidity flags as input features for mortality, readmission, or complication prediction models
• Population health analytics: Identify high-risk subpopulations for care management programs
• Quality measurement: Calculate risk-adjusted O:E ratios for facility performance comparison
• EHR integration: Score patients prospectively from problem list ICD-10 codes for care planning

Citation

@software{elixhauser_ahrq_2026,
  author = {Opelka, Frank G.},
  title = {elixhauser-ahrq: Python Implementation of the AHRQ Elixhauser Comorbidity Index},
  version = {2026.1},
  year = {2026},
  organization = {Episodes of Care Solutions (EOCS)},
  url = {https://github.com/fopelka-EOCS/elixhauser-ahrq},
  note = {Based on AHRQ HCUP Elixhauser Comorbidity Software Refined for ICD-10-CM. CDC-reviewed.}
}

References

• Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Medical Care 1998;36(1):8–27.
• van Walraven C, Austin PC, Jennings A, Quan H, Forster AJ. A modification of the Elixhauser comorbidity measures into a point system for hospital death using administrative data. Medical Care 2009;47(6):626–633.
• Moore BJ, White S, Washington R, Coenen N, Elixhauser A. Identifying increased risk of readmission and in-hospital mortality using hospital administrative data. Medical Care 2017;55(7):698–705.
• AHRQ HCUP: https://hcup-us.ahrq.gov/toolssoftware/comorbidityicd10/comorbidity_icd10.jsp