Pyhealth

If you're building clinical ML pipelines and tired of reinventing EHR preprocessing, this wraps PyHealth's opinionated five-stage workflow: dataset, task definition, model instantiation, training, metrics. It handles MIMIC-III/IV, eICU, OMOP, sleep signals, and chest X-rays out of the box, plus a stable of architectures like Transformer, RETAIN, and SafeDrug. The real value is in the task library (mortality, readmission, drug recommendation, ICD coding) and the medical code utilities for mapping between ICD-9/10, ATC, NDC, and RxNorm vocabularies. It's prescriptive by design, so if your workflow fits the mold you save weeks of plumbing. If it doesn't, you'll fight it. The skill emphasizes the critical gotchas: models need SampleDatasets not BaseDatasets, always split by patient to avoid leakage, and match your monitor metric to the task type.