27 citations · 40 across the 10 of their papers we have counts for
11 papers
MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems
Shruthi Chari, Oshani Seneviratne, Prithwish Chakraborty +2
Explanations are crucial for building trustworthy AI systems, but a gap often exists between the explanations provided by models and those needed by users. To address this gap, we…
Improving Primary Healthcare Workflow Using Extreme Summarization of Scientific Literature Based on Generative AI
Gregor Stiglic, Leon Kopitar, Lucija Gosak +5
Primary care professionals struggle to keep up to date with the latest scientific literature critical in guiding evidence-based practice related to their daily work. To help solve…
Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes
Shruthi Chari, Prasant Acharya, Daniel M. Gruen +8
Medical experts may use Artificial Intelligence (AI) systems with greater trust if these are supported by contextual explanations that let the practitioner connect system inference…
Distillation to Enhance the Portability of Risk Models Across Institutions with Large Patient Claims Database
Steve Nyemba, Chao Yan, Ziqi Zhang +4
Artificial intelligence, and particularly machine learning (ML), is increasingly developed and deployed to support healthcare in a variety of settings. However, clinical decision s…
Leveraging Clinical Context for User-Centered Explainability: A Diabetes Use Case
Shruthi Chari, Prithwish Chakraborty, Mohamed Ghalwash +7
Academic 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 interac…
Disease Progression Modeling Workbench 360
Parthasarathy Suryanarayanan, Prithwish Chakraborty, Piyush Madan +9
In this work we introduce Disease Progression Modeling workbench 360 (DPM360) opensource clinical informatics framework for collaborative research and delivery of healthcare AI. DP…
Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare
Chang Lu, Chandan K. Reddy, Prithwish Chakraborty +2
Accurate and explainable health event predictions are becoming crucial for healthcare providers to develop care plans for patients. The availability of electronic health records (E…
Blending Knowledge in Deep Recurrent Networks for Adverse Event Prediction at Hospital Discharge
Prithwish Chakraborty, James Codella, Piyush Madan +22
Deep learning architectures have an extremely high-capacity for modeling complex data in a wide variety of domains. However, these architectures have been limited in their ability…
Phenotypical Ontology Driven Framework for Multi-Task Learning
Mohamed Ghalwash, Zijun Yao, Prithwish Chakraborty +2
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…
ODVICE: An Ontology-Driven Visual Analytic Tool for Interactive Cohort Extraction
Mohamed Ghalwash, Zijun Yao, Prithwish Chakrabotry +2
Increased availability of electronic health records (EHR) has enabled researchers to study various medical questions. Cohort selection for the hypothesis under investigation is one…
G-Net: A Deep Learning Approach to G-computation for Counterfactual Outcome Prediction Under Dynamic Treatment Regimes
Rui Li, Zach Shahn, Jun Li +5
Counterfactual prediction is a fundamental task in decision-making. G-computation is a method for estimating expected counterfactual outcomes under dynamic time-varying treatment s…