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dc.contributor.authorChari, Shruthi
dc.contributor.authorChakraborty, Prithwish
dc.contributor.authorGhalwash, Mohamed
dc.contributor.authorSeneviratne, Oshani
dc.contributor.authorEyigöz, Elif
dc.contributor.authorGruen, Daniel
dc.contributor.authorSuarez Saiz, Fernando
dc.contributor.authorChen, Ching Hua
dc.contributor.authorMeyer Rojas, Pablo
dc.contributor.authorMcGuinness, Deborah
dc.description.abstractAcademic advances of AI models in high-precision domains, like healthcare, need to be made explainable in order to enhance real-world adoption. Our past studies and ongoing interactions indicate that medical experts can use AI systems with greater trust if there are ways to connect the model inferences about patients to explanations that are tied back to the context of use. Specifically, risk prediction is a complex problem of diagnostic and interventional importance to clinicians wherein they consult different sources to make decisions. 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. We validate the importance of these dimensions by implementing a proof-of-concept (POC) in type-2 diabetes (T2DM) use case where we assess the risk of chronic kidney disease (CKD) - a common T2DM comorbidity. Within the POC, we include risk prediction models for CKD, post-hoc explainers of the predictions, and other natural-language modules which operationalize domain knowledge and CPGs to provide context. With primary care physicians (PCP) as our end-users, we present our initial results and clinician feedback in this paper. Our POC approach covers multiple knowledge sources and clinical scenarios, blends knowledge to explain data and predictions to PCPs, and received an enthusiastic response from our medical expert.
dc.titleLeveraging Clinical Context for User-Centered Explainability: A Diabetes Use Case

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