healthsim-patientsim
Generate realistic clinical patient data including demographics, encounters, diagnoses, medications, labs, and vitals. Use when user requests: (1) patient records or clinical data, (2) EMR test data, (3) specific clinical cohorts like diabetes or heart failure, (4) HL7v2 or FHIR patient resources.
Generative Framework
Conversation-driven specification and execution of healthcare data generation at scale
pyhealth
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).
