Individual skill
pyhealth
Builds clinical deep-learning pipelines with PyHealth using its Dataset → Task → Model → Trainer → Metrics pattern. Covers loading MIMIC-III/IV, eICU, OMOP, SleepEDF, ChestXray14 and EHRShot data, defining prediction tasks (mortality, readmission, length of stay, drug recommendation, sleep staging, ICD coding, EEG events), models such as Transformer, RETAIN, GAMENet, SafeDrug and StageNet, training with Trainer, and ICD/ATC/NDC/RxNorm code lookup and cross-mapping. Use when building a clinical prediction model on EHR data. Use when working with MIMIC or eICU datasets. Use when recommending drugs or staging sleep from signals. Use when mapping medical codes between ICD, ATC, NDC or RxNorm. Not for generic PyTorch on tabular data.
plugins/KalarisLabs/research-agent-skills/skills/pyhealth/SKILL.md
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- Apache-2.0
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SKILL.mdcore · 9,355 bytesSKILL.mdcore · 9,355 bytesSKILL.mdcore · 9,355 bytesSKILL.mdcore · 9,355 bytesSKILL.mdcore · 9,355 bytesSKILL.mdcore · 9,355 bytesassets/starter_pipeline.pyruns · 2,338 bytesreferences/datasets.mddoc · 4,892 bytesreferences/examples.mddoc · 8,137 bytesreferences/installation.mddoc · 3,972 bytesreferences/medcode.mddoc · 3,394 bytesreferences/models.mddoc · 4,551 bytesreferences/tasks.mddoc · 5,267 bytes
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# PyHealth
PyHealth (https://pyhealth.dev/) is a Python toolkit for clinical deep learning. It provides a unified, modular pipeline across electronic health records (EHR), physiological signals, and medical imaging.
The library is built around a **5-stage pipeline** — `Dataset → Task → Model → Trainer → Metrics` — where each stage is replaceable and the interfaces between stages are stable. Code that follows this pipeline shape composes well; code that bypasses it usually fights the library.
## When to use this skill
Use this skill whenever the user is doing clinical/healthcare ML and any of the following are true:
- They mention PyHealth, MIMIC-III/IV, eICU, OMOP-CDM, EHRShot, SleepEDF, SHHS, ISRUC, COVID19-CXR, ChestX-ray14, TUEV/TUAB.
- They want to predict mortality, readmission, length of stay, drug recommendations, sleep stages, ICD codes, EEG events, or de-identification.
- They need to look up or cross-map medical codes (ICD-9-CM, ICD-10-CM, ATC, NDC, RxNorm, CCS).
- They have EHR-shaped data and want to train a clinical model without writing the plumbing themselves.