PyHealth

PyHealth

Overview

PyHealth provides an end-to-end pipeline for healthcare ML on EHR data: data loading → medical code processing → patient-level dataset construction → model training → evaluation. It natively supports MIMIC-III, MIMIC-IV, eICU-CRD, and OMOP-CDM structured databases, and handles the idiosyncratic data formats of each. Medical codes (ICD-9, ICD-10, ATC, NDC, SNOMED) are organized in a hierarchical code system that supports code-level embedding and cross-ontology mapping. Pre-built tasks — mortality prediction, drug recommendation, readmission, length-of-stay, diagnosis code prediction — can be instantiated in a few lines. Custom tasks follow a standardized interface.

When to Use

• Training clinical outcome prediction models (mortality, readmission, LOS) from MIMIC-III or MIMIC-IV
• Building drug recommendation or drug interaction prediction models using ATC code hierarchy
• Processing OMOP-CDM formatted data from institutional EHR systems for ML
• Using pretrained clinical models (RETAIN, GRASP, MedBERT) as baselines on healthcare benchmarks
• Constructing patient visit sequences with temporal structure for RNN/Transformer models
• Evaluating clinical prediction models with appropriate metrics (AUROC, AUPRC, F1, Jaccard)
• Use FIDDLE for pure EHR preprocessing without ML; use clinical-longformer for clinical note NLP

Prerequisites

• Python packages: pyhealth, torch, pandas, scikit-learn
• Data requirements: MIMIC-III/IV CSV files (requires PhysioNet credentialing), eICU, or OMOP-CDM database
• MIMIC access: request at physionet.org (free; requires CITI training, ~1 week)

pip install pyhealth torch pandas scikit-learn
# Download MIMIC-III: https://physionet.org/content/mimiciii/
# Download MIMIC-IV: https://physionet.org/content/mimiciv/

Quick Start

from pyhealth.datasets import MIMIC3Dataset

# Load MIMIC-III (specify path to downloaded CSV files)
dataset = MIMIC3Dataset(
    root="path/to/mimic-iii/",
    tables=["DIAGNOSES_ICD", "PRESCRIPTIONS", "PROCEDURES_ICD"],
    code_mapping={"ICD9CM": "CCSCM"},  # map ICD-9 codes to CCS multi-level
    dev=True,  # dev=True uses 1% of data for fast testing
)