用原型学习提升医疗预测可解释性，让医生能看懂、信得过。
Prototype-Based Learning for Healthcare: A Demonstration of Interpretable AI
- 基于原型的学习框架，用典型病例类比辅助决策。
- 在医疗数据上表现优于传统模型，且结果直观可解释。
- 适合需要透明决策的临床场景，尤其医生与患者共同参与时。
尽管机器学习和可解释AI取得进展，个性化预防性医疗仍存在不足：预测、干预和建议需对所有医疗利益相关方均具备可理解性和可验证性。本文展示原型学习如何解决这一需求。提出的框架ProtoPal包含前后端模式，在实现优异定量性能的同时，提供干预措施及其模拟结果的直观呈现，使决策过程透明可信。
原文摘要 · Abstract (English)
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.
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