Search Results for author: Mohamed Ghalwash

Found 9 papers, 0 papers with code

Simpler Calibration for Survival Analysis

no code implementations29 Sep 2021 Hiroki Yanagisawa, Toshiya Iwamori, Akira Koseki, Michiharu Kudo, Mohamed Ghalwash, Prithwish Chakraborty

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.

regression Survival Analysis

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

no code implementations6 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.

Disease Progression Modeling Workbench 360

no code implementations24 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.

BIG-bench Machine Learning

Phenotypical Ontology Driven Framework for Multi-Task Learning

no code implementations4 Sep 2020 Mohamed Ghalwash, Zijun Yao, Prithwish Chakraborty, James Codella, Daby Sow

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.

Multi-Task Learning

ODVICE: An Ontology-Driven Visual Analytic Tool for Interactive Cohort Extraction

no code implementations13 May 2020 Mohamed Ghalwash, Zijun Yao, Prithwish Chakrabotry, James Codella, Daby Sow

Increased availability of electronic health records (EHR) has enabled researchers to study various medical questions.

Data Augmentation


no code implementations27 Sep 2018 Daby Sow, Mohamed Ghalwash, Zach Shahn, Sanjoy Dey, Moulay Draidia, Li-wei Lehmann

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.

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