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  • Accepted Manuscript: Counterfactual and Factual Reasoning over Hypergraphs for Interpretable Clinical Predictions on EHR
Title: Counterfactual and Factual Reasoning over Hypergraphs for Interpretable Clinical Predictions on EHR
Electronic Health Record modeling is crucial for digital medicine. However, existing models ignore higher-order interactions among medical codes and their causal relations towards downstream clinical predictions. To address such limitations, we propose a novel framework CACHE, to provide effective and insightful clinical predictions based on hypergraph representation learning and counterfactual and factual reasoning techniques. Experiments on two real EHR datasets show the superior performance of CACHE. Case studies with a domain expert illustrate a preferred capability of CACHE in generating clinically meaningful interpretations towards the correct predictions.
Authors:
; ; ; ; ;
Award ID(s):
1838200 2145411
Publication Date:
NSF-PAR ID:
10391649
Journal Name:
Proceedings of the 2nd Machine Learning for Health symposium
Volume:
193
Page Range or eLocation-ID:
259-278
Sponsoring Org:
National Science Foundation
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