Sparse ProtoPatient: Interactive Multi-Prototype Explanations for Clinical Diagnosis Prediction

Abstract

We present the Sparse ProtoPatient Demo, a publicly available interactive system for interpretable ICD-10 diagnosis prediction from clinical admission notes. The system is designed for clinicians in training, researchers, and educators exploring prototype-based diagnostic reasoning. The demo links predictions to learned prototypical patient representations and token-level evidence, allowing users to input custom text or select preset cases, inspect predicted ICD-10 codes, visualize label-wise saliency, retrieve supporting prototype notes, and compare alternative prototype cohorts. The demo provides a reproducible platform for interactive inspection of prototype-based clinical reasoning, enabling complementary opinion exploration, model auditing, and teaching of interpretable diagnosis prediction. The deployed model is trained on the publicly released CodiEsp corpus (1000 clinical notes, 955 ICD-10 labels) using a sparse multi-prototype architecture with five prototypes per label. We use the official machine-translated English CodiEsp-MT release to support English-language interaction. It achieves a macro-AUROC of 0.92 on a held-out test set and supports real-time interaction (300ms per query). The system is fully containerized for public research and educational use.