Agent skill · research science · aiskillstore
pyhealth
Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).
Why this skill is useful
Adds specialized healthcare AI models and a modular pipeline for clinical data processing that the AI cannot generate on its own.
What it needs
Requires python installed locally. About 8k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
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 Core Capabilities PyHealth operates through a modular 5-stage pipeline optimized for healthcare AI: 1. Data Loading: Access 10+ healthcare datasets with standardized interfaces 2. Task Definition: Apply 20+ predefined clinical prediction tasks or create custom tasks 3. Model Selection: Choose from 33+ models (baselines, deep learning, healthcare-specific) 4. Training: Train with automatic checkpointing, monitoring, and evaluation 5. Deployment: Calibrate, interpret, and validate for clinical use Performance: 3x faster than pandas for healthcare data processing Quick Start Workflow Detailed Documentation This skill includes comprehensive reference documentation organized by functionality. Read specific reference files as needed: 1. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/pyhealth