An interpretable prototype parts-based neural network for medical tabular data
The ability to interpret machine learning model decisions is critical in such domains as healthcare, where trust in model predictions is as important as their accuracy. Inspired by the development of prototype parts-based deep neural networks in computer vision, we propose a new model for tabular data, specifically tailored to medical records, that requires discretization of diagnostic result norms. Unlike the original vision models that rely on the spatial structure, our method employs trainable patching over features describing a patient, to learn meaningful prototypical parts from structured data. These parts are represented as binary or discretized feature subsets. This allows the mod...
| Authors | Jacek Karolczak, Jerzy Stefanowski |
|---|---|
| Primary category | cs.LG |
| Published | 5 March 2026 |
| Last updated at source | 5 March 2026 |
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