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.
Therefore, X-CAL has recently been proposed for the calibration, which is supposed to be used as a regularization term in the loss function of a neural network.
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.
no code implementations • 24 Jun 2021 • Parthasarathy Suryanarayanan, Prithwish Chakraborty, Piyush Madan, Kibichii Bore, William Ogallo, Rachita Chandra, Mohamed Ghalwash, Italo Buleje, Sekou Remy, Shilpa Mahatma, Pablo Meyer, Jianying Hu
In this work we introduce Disease Progression Modeling workbench 360 (DPM360) opensource clinical informatics framework for collaborative research and delivery of healthcare AI.
no code implementations • 9 Apr 2021 • Prithwish Chakraborty, James Codella, Piyush Madan, Ying Li, Hu Huang, Yoonyoung Park, Chao Yan, Ziqi Zhang, Cheng Gao, Steve Nyemba, Xu Min, Sanjib Basak, Mohamed Ghalwash, Zach Shahn, Parthasararathy Suryanarayanan, Italo Buleje, Shannon Harrer, Sarah Miller, Amol Rajmane, Colin Walsh, Jonathan Wanderer, Gigi Yuen Reed, Kenney Ng, Daby Sow, Bradley A. Malin
Deep learning architectures have an extremely high-capacity for modeling complex data in a wide variety of domains.
Despite the large number of patients in Electronic Health Records (EHRs), the subset of usable data for modeling outcomes of specific phenotypes are often imbalanced and of modest size.
Increased availability of electronic health records (EHR) has enabled researchers to study various medical questions.
Counterfactual prediction is a fundamental task in decision-making.
Learning explainable patient temporal embeddings from observational data has mostly ignored the use of RNN architecture that excel in capturing temporal data dependencies but at the expense of explainability.