Build clinical ML pipelines with datasets, tasks, models, and trainers.
npx skills add https://github.com/crazymsn/academic-skills --skill pyhealth-crazymsn
Please help me install this Agent Skill. Skill: pyhealth Source: https://github.com/crazymsn/academic-skills/tree/main/academic-skills/pyhealth Command: npx skills add https://github.com/crazymsn/academic-skills --skill pyhealth-crazymsn
PyHealth provides a unified, modular toolkit to build end-to-end clinical deep-learning pipelines, from raw EHR/clinical data to trained models and evaluation metrics.
Run the starter pipeline to see an end-to-end MortalityPredictionMIMIC3 workflow from dataset to evaluation.
High-intent search queries and answers about installing and using this skill.
To build a clinical ML pipeline for MIMIC-III, use a standardized 5-stage workflow: Dataset, Task, Model, Trainer, and Metrics. This structure maps raw EHR data to tasks like mortality prediction and trains models while computing clinically relevant evaluation metrics.
Clinical machine learning tasks support datasets including MIMIC-III/IV, eICU, OMOP, SleepEDF, ChestXray14, and EHRShot. You can apply these to mortality, readmission, length of stay, drug recommendation, sleep staging, and ICD/ATC/RxNorm medical coding workflows.
Set up a deep learning pipeline for medical coding by defining a Dataset, Task, Model, Trainer, and Metrics. This modular approach processes clinical EHR data and handles ICD, ATC, and RxNorm coding tasks end-to-end without extensive boilerplate.
Yes, the toolkit supports sleep staging using SleepEDF data. It unifies signal data processing through the same modular pipeline structure, mapping raw inputs to trained models and evaluation metrics for sleep stage classification.
Yes, you can use OMOP CDM data for patient readmission prediction. The toolkit maps OMOP formatted EHR records to readmission tasks, training deep learning models and evaluating outcomes using standardized clinical metrics.
The best way to reduce boilerplate in healthcare ML workflows is using a modular component system with unified dataset, task, model, trainer, and metrics stages. This standardizes pipelines across EHR, signal, and imaging data to accelerate end-to-end prototyping.
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