ProSeNet learns a sparse set of prototypes for case-based explanations in deep sequence models, matches state-of-the-art accuracy on several tasks, and supports manual prototype refinement by non-experts.
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UNVERDICTED 2representative citing papers
A behavioral engagement scoring method predicts patient response propensity in care management using real-world data and supplies interpretable insights via prototypical patients without performance loss.
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Interpretable and Steerable Sequence Learning via Prototypes
ProSeNet learns a sparse set of prototypes for case-based explanations in deep sequence models, matches state-of-the-art accuracy on several tasks, and supports manual prototype refinement by non-experts.
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Learning Patient Engagement in Care Management: Performance vs. Interpretability
A behavioral engagement scoring method predicts patient response propensity in care management using real-world data and supplies interpretable insights via prototypical patients without performance loss.