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.

翻译：尽管机器学习和可解释人工智能领域近期取得了进展，个性化预防性医疗保健仍存在一个缺口：预测、干预措施和建议必须同时满足医疗保健领域所有利益相关者的可理解性与可验证性要求。本文展示了原型学习如何满足这些需求。我们提出的ProtoPal框架具备前端与后端双模式；该框架在取得优异量化性能的同时，还能以直观方式呈现干预措施及其模拟结果。