Prototype-Based Learning for Healthcare: A Demonstration of Interpretable AI

TL;DR

This paper demonstrates how prototype-based learning, through the ProtoPal framework, can enhance interpretability and performance in personalized healthcare predictions and interventions.

Contribution

It introduces ProtoPal, a novel prototype-based framework that improves interpretability and effectiveness in healthcare AI applications.

Findings

ProtoPal achieves superior quantitative performance.

It provides intuitive presentation of interventions.

It offers verifiable and understandable healthcare predictions.

Abstract

Despite recent advances in machine learning and explainable AI, a gap remains in personalized preventive healthcare: predictions, interventions, and recommendations should be both understandable and verifiable for all stakeholders in the healthcare sector. We present a demonstration of how prototype-based learning can address these needs. Our proposed framework, ProtoPal, features both front- and back-end modes; it achieves superior quantitative performance while also providing an intuitive presentation of interventions and their simulated outcomes.