ehr-analysis
End-to-end EHR predictive modeling pipeline with PyHealth, covering dataset loading, task definition, model training, evaluation, calibration, and clinical interpretation.
npx skills add BioTender-max/awesome-bio-agent-skills --skill ehr-analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 modeli
- Version Compatibility
- Overview
- When To Use This Skill
- Quick Route
- Progressive Disclosure
- Default Rules
- Expected Inputs
- Expected Outputs
- Preferred Tools
- Starter Pattern
- Workflow
- 1. Validate dataset access and structure
- 2. Load data with pyhealth.datasets
- 3. Define the clinical task
What does the ehr-analysis skill do?
End-to-end EHR predictive modeling pipeline with PyHealth, covering dataset loading, task definition, model training, evaluation, calibration, and clinical interpretation.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill ehr-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.