Preprint • 2024
Amoebanator: Early Risk Triage with Calibration, Decision Curves, and Transparent Thresholds
J. Montenegro, Collaborators
clinical MLcalibrationdecision curvesexplainability
Abstract
We present a lightweight, well-documented clinical ML prototype that emphasizes calibrated risk, transparent thresholds via net benefit, and a reproducibility stack (model card, dataset card, and interactive demo).
Key contributions
- Conformal-style safety control of false negatives during threshold selection
- Decision-curve driven tuning for pragmatic operating points
- Full reproducibility: interactive demo, model card, dataset card