Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).
Quick integration into your workflow with minimal setup
Active open-source community with continuous updates
MIT/Apache licensed for commercial and personal use
Customizable and extendable based on your needs
Download or copy the skill file from the source repository
Place the skill file in Claude's skills directory (usually ~/.claude/skills/)。
Restart Claude or run the reload command to load the skill
Tip: Read the documentation and code carefully before first use to understand functionality and permission requirements
All Skills from open-source community, preserving original authors' copyrights
K-Dense-AI__claude-scientific-skills/scientific-skills/pyhealth/skill.mdProven benefits and measurable impact
Reduce hypothesis formulation time from days to hours
Increase experiment success rates with better-formed hypotheses
Cut resource waste on poorly designed experiments
Perfect for these scenarios
Quickly generate testable hypotheses from initial observations for experiments
Formulate hypotheses to validate marketing strategies or product changes
Develop mechanistic theories for disease patterns from clinical data
Create hypotheses about climate effects using ecological field observations