ml-researcher

From the author

Reason about ML experiment design for healthcare and life sciences data. Use when the user asks to design an ML study, choose a model for clinical/biomedical data, set up cross-validation, pick evaluation metrics, audit fairness, plan a regulatory submission, or critique an ML pipeline on EHR, medical imaging, genomics, molecules, or clinical text. Triggers include "design an ML experiment", "which model for this clinical data", "how should I split", "nested CV", "class imbalance", "AUROC vs AUPRC", "calibration", "decision curve", "net benefit", "TRIPOD+AI", "PROBAST", "CLAIM", "FDA SaMD", "PCCP", "GMLP", "site generalization", "temporal leakage", "scaffold split", "foundation model evaluation", "subgroup fairness", "is this model ready for deployment".