Pyhealth

If you're building clinical ML models, this gives you the full stack: 10+ healthcare datasets (MIMIC-III/IV, eICU, OMOP), 20+ predefined prediction tasks (mortality, readmission, drug recommendation), and 33+ models including healthcare-specific architectures like RETAIN and SafeDrug. The medical coding translation alone is worth it, handling ICD-9/10, NDC, ATC, and RxNorm conversions that would otherwise take days to implement. It's opinionated about the five-stage pipeline (load, task, model, train, deploy) but that structure actually makes sense for clinical work. Claims 3x faster than pandas for healthcare data processing. The fairness metrics and calibration tools show they're thinking about real deployment, not just academic benchmarks.

skills.sh installs: 325
GitHub stars: 33.2K
last updated: Aug 21, 2026

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Install

$ npx -y skills add davila7/claude-code-templates --skill pyhealth --agent claude-code

Installs into .claude/skills of the current project.

Files

PyHealth: Healthcare AI Toolkit

Overview

PyHealth is a comprehensive Python library for healthcare AI that provides specialized tools, models, and datasets for clinical machine learning. Use this skill when developing healthcare prediction models, processing clinical data, working with medical coding systems, or deploying AI solutions in healthcare settings.

When to Use This Skill

Invoke this skill when:

Working with healthcare datasets: MIMIC-III, MIMIC-IV, eICU, OMOP, sleep EEG data, medical images
Clinical prediction tasks: Mortality prediction, hospital readmission, length of stay, drug recommendation
Medical coding: Translating between ICD-9/10, NDC, RxNorm, ATC coding systems
Processing clinical data: Sequential events, physiological signals, clinical text, medical images
Implementing healthcare models: RETAIN, SafeDrug, GAMENet, StageNet, Transformer for EHR
Evaluating clinical models: Fairness metrics, calibration, interpretability, uncertainty quantification