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).
8.7
Rating
0
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Machine Learning
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Exceptional healthcare AI skill with comprehensive coverage of PyHealth library capabilities. The SKILL.md provides crystal-clear guidance on when to invoke the skill (6 specific trigger scenarios), a well-organized quick-start workflow, and 6 detailed use cases mapping problems to reference files. The structure is exemplary: concise main file (manageable length) with clear signposting to 6 detailed reference files covering datasets, medical coding, tasks, models, preprocessing, and training/evaluation. Task knowledge is outstanding with complete code examples, best practices, troubleshooting guidance, and 28,000 words of modular documentation. The skill addresses a genuinely complex domain (healthcare AI with EHR data, medical coding systems, clinical prediction) where a CLI agent would struggle significantly with token costs and domain expertise. Minor point: while highly specialized, the core value is organizing an existing library rather than novel algorithms, but the healthcare domain complexity and comprehensive task breakdown still provide substantial value. A CLI agent would require extensive research and many iterations to achieve what this skill provides immediately.
davila7
Skill Author
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