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).
8.3
Rating
0
Installs
Machine Learning
Category
Exceptional healthcare AI skill with comprehensive coverage of PyHealth's capabilities. The description is precise and actionable, clearly specifying when to invoke the skill (EHR data, clinical predictions, medical coding, etc.). Task knowledge is outstanding with detailed quick-start code, complete workflows, 6 use cases, and references to modular documentation files covering datasets, models, tasks, preprocessing, medical coding, and training/evaluation. Structure is excellent—SKILL.md provides a clear overview and index while delegating detailed content to organized reference files (28,000 words total), avoiding clutter. Novelty is high: healthcare AI requires specialized domain knowledge (medical coding systems, clinical datasets like MIMIC-IV, healthcare-specific models like RETAIN/SafeDrug, fairness/calibration for clinical deployment) that would be token-intensive and error-prone for a CLI agent to reconstruct. The skill meaningfully reduces complexity by packaging domain expertise, standardized workflows, and clinical best practices. Minor: could slightly improve structure clarity by adding brief summaries of what each reference file contains in the main documentation section.
K-Dense-AI
Skill Author
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