Prototype-Based Learning for Healthcare: A Demonstration of Interpretable AI
By: Ashish Rana , Ammar Shaker , Sascha Saralajew and more
Potential Business Impact:
Shows how to prevent sickness with clear, proven steps.
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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