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Found 4 papers, 1 papers with code
Leveraging Clinical Context for User-Centered Explainability: A Diabetes Use Case
To enable the adoption of the ever improving AI risk prediction models in practice, we have begun to explore techniques to contextualize such models along three dimensions of interest: the patients' clinical state, AI predictions about their risk of complications, and algorithmic explanations supporting the predictions.
Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph
Food recommendation has become an important means to help guide users to adopt healthy dietary habits.
A Framework for Generating Explanations from Temporal Personal Health Data
Whereas it has become easier for individuals to track their personal health data (e. g., heart rate, step count, food log), there is still a wide chasm between the collection of data and the generation of meaningful explanations to help users better understand what their data means to them.
Learning Patient Engagement in Care Management: Performance vs. Interpretability
In this paper, we present a real world data-driven method and the behavioral engagement scoring pipeline for scoring the engagement level of a patient in two regards: (1) Their interest in enrolling into a relevant care program, and (2) their interest and commitment to program goals.
