Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes

no code implementations • 11 Feb 2023 • Shruthi Chari, Prasant Acharya, Daniel M. Gruen, Olivia Zhang, Elif K. Eyigoz, Mohamed Ghalwash, Oshani Seneviratne, Fernando Suarez Saiz, Pablo Meyer, Prithwish Chakraborty, Deborah L. McGuinness

All of these steps were performed in engagement with medical experts, including a final evaluation of the dashboard results by an expert medical panel.

Question Answering

Leveraging Clinical Context for User-Centered Explainability: A Diabetes Use Case

no code implementations • 6 Jul 2021 • Shruthi Chari, Prithwish Chakraborty, Mohamed Ghalwash, Oshani Seneviratne, Elif K. Eyigoz, Daniel M. Gruen, Fernando Suarez Saiz, Ching-Hua Chen, Pablo Meyer Rojas, Deborah L. McGuinness

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.