Medical & Healthcare
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.
- Publisher / source
- KalarisLabs/research-agent-skills (found on the CSV list)
- Upstream record
- KalarisLabs/research-agent-skills
- Version
- Version 2
- Published
- Publication review
- Approved for publication
- Content fingerprint
7ac9cdfcf8de92adf877df71aa8d0b18f16b2c16725780b336bab0673b2c8b4f