ehr-analysislisted

# EHR Analysis ## Version Compatibility Reference examples assume: - `pyhealth` 1.1.6+ (stable) or 2.0+ (latest, requires Python ≥ 3.12) - `torch` 2.0+ - `pandas` 2.0+ Verify the runtime first: - Python: `python -c "import pyhealth; print(pyhealth.__version__)"` - CLI: `pip show pyhealth` - If APIs differ between 1.x and 2.x, consult the installed docs and adapt the pattern accordingly. ## Overview Use this skill when the user needs to: - load and structure EHR data from MIMIC-III, MIMIC-IV, eICU, OMOP-CDM, or a custom dataset - define clinical prediction tasks such as mortality, readmission, length of stay, or drug recommendation - train and evaluate deep learning or ML models on patient visit sequences - apply medical code normalization, ontology mapping, or tokenization - calibrate model outputs or generate post-hoc interpretability artifacts ## When To Use This Skill - the data source is a structured EHR database or tabular patient record export - the task requires longitudinal patient visit sequences as model input - the user needs benchmark-compatible evaluation metrics for clinical AI - medical code systems (ICD, ATC, NDC, CPT, CCS) must be normalized or mapped before modeling ## Quick Route - Known EHR source (MIMIC, eICU, OMOP): load with a `pyhealth.datasets` class, then `set_task`. - Custom tabular data: wrap in `SampleBaseDataset`, then follow the same pipeline. - Code mapping only: use `pyhealth.medcode` independently without the full pipeline. - Eva