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pyhealth

Build clinical deep-learning pipelines with a modular 5-stage structure.

Updated May 17, 2026
Search Tags:#data science#deep learning#healthcare#clinical prediction#mortality prediction
One-click install
npx skills add https://github.com/galeep/plugin-place --skill pyhealth-galeep
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pyhealth
Source: https://github.com/galeep/plugin-place/tree/main/plugins/sci-healthcare-ai/skills/pyhealth
Command: npx skills add https://github.com/galeep/plugin-place --skill pyhealth-galeep

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pyhealth, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill streamlines the creation and management of clinical/healthcare deep-learning pipelines, making it easier to build, train, and evaluate models for healthcare applications.

Core Features & Use Cases

  • Unified Pipeline: Provides a modular 5-stage pipeline (Dataset → Task → Model → Trainer → Metrics) for building deep-learning pipelines.
  • Dataset Support: Offers support for a variety of datasets including MIMIC, eICU, OMOP, SleepEDF, ChestXray14, and EHRShot.
  • Modeling: Supports various models such as Transformer, RETAIN, GAMENet, SafeDrug, MICRON, StageNet, AdaCare, CNN/RNN/MLP.
  • Use Case: For a hospital looking to predict patient mortality based on EHR data, PyHealth can be used to load the data, define the task, instantiate the model, train the model, and compute clinical metrics.

Quick Start

Use the pyhealth skill to train a model for mortality prediction on MIMIC-III data.

Frequently Asked Questions about pyhealth

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a clinical prediction pipeline for patient mortality using EHR data?▼

To build a clinical prediction pipeline, use the modular 5-stage workflow (Dataset → Task → Model → Trainer → Metrics) to load EHR data, define the mortality prediction task, instantiate a model, train it, and compute clinical metrics.

What deep learning models are supported for healthcare prediction tasks?▼

Supported deep learning models for healthcare prediction include Transformer, RETAIN, GAMENet, SafeDrug, MICRON, StageNet, AdaCare, and standard CNN/RNN/MLP architectures.

Can I use MIMIC and eICU datasets for clinical deep learning workflows?▼

Yes, you can use MIMIC and eICU datasets for clinical deep learning workflows, alongside OMOP, SleepEDF, ChestXray14, and EHRShot datasets within the unified pipeline.

What is the best way to structure a healthcare deep learning project?▼

The best way to structure a healthcare deep learning project is using a modular 5-stage pipeline covering Dataset, Task, Model, Trainer, and Metrics to streamline model construction and evaluation.

Do I need specific clinical datasets to train deep learning models for mortality prediction?▼

You need access to clinical datasets like MIMIC-III or eICU containing EHR data to effectively train and evaluate deep learning models for mortality prediction tasks.

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