Individual skill
pyhealth
Builds and validates PyHealth clinical machine-learning pipelines for EHR, signals, imaging, and medical codes. Use for PyHealth dataset loading, MIMIC-III/IV, eICU or OMOP prediction tasks, patient-level evaluation, mortality/readmission/length-of-stay modeling, medication recommendation, sleep staging, Trainer checkpoints, and ICD/ATC/NDC/RxNorm mapping.
skills/pyhealth/SKILL.md
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SKILL.mdcore · 8,227 bytesSKILL.mdcore · 8,227 bytesSKILL.mdcore · 8,227 bytesSKILL.mdcore · 8,227 bytesassets/starter_pipeline.pyruns · 3,870 bytesreferences/datasets.mddoc · 5,343 bytesreferences/examples.mddoc · 7,099 bytesreferences/installation.mddoc · 4,524 bytesreferences/medcode.mddoc · 3,645 bytesreferences/models.mddoc · 5,888 bytesreferences/tasks.mddoc · 6,610 bytes
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# PyHealth
Targets **PyHealth 2.0.2**, verified against its released wheel, current official
documentation, and CPU execution on Python 3.12/Torch 2.7.1. PyHealth's pipeline is
`Dataset -> Task -> Model -> Trainer -> Metrics`; its 1.x and 2.x interfaces differ.
Do not combine legacy `Visit` examples with the 2.x event/processor API.
## When to use
Use for clinical prediction with PyHealth, including EHR sequences, physiological
signals, imaging tasks, or medical-code lookup. Establish the cohort, prediction
time, observation window, outcome horizon, and unit of evaluation before modeling.
For general tabular learning without a PyHealth dataset/task, this skill is optional.
