Peer-Reviewed Publication
Health Inf Sci Syst2026;14(1):7.December 1, 2026Journal Article
CURENet: combining unified representations for efficient chronic disease prediction.
Cong-Tinh Dao1,2, Nguyen Minh Thao Phan1,2, Jun-En Ding3, Chenwei Wu4, David Restrepo5, Dongsheng Luo6, Fanyi Zhao3, Chun-Chieh Liao3, Wen-Chih Peng1, Chi-Te Wang7, Pei-Fu Chen7,8, Ling Chen1, Xinglong Ju9, Feng Liu3, Fang-Ming Hung7
1National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
2Can Tho University, Can Tho, Vietnam.
3Stevens Institute of Technology, Can Tho, USA.
4University of Michigan, Ann Arbor, USA.
5Massachusetts Institute of Technology, Cambridge, USA.
6Florida International University, Miami, USA.
7Far Eastern Memorial Hospital, New Taipei, Taiwan.
8Yuan Ze University, Taoyuan, Taiwan.
9Southern Utah University, Cedar, USA.
Abstract
Electronic health records (EHRs) are designed to synthesize diverse data types, including unstructured clinical notes, structured lab tests, and time-series visit data. Physicians draw on these multimodal and temporal sources of EHR data to form a comprehensive view of a patient's health, which is crucial for informed therapeutic decision-making. Yet, most predictive models fail to fully capture t…
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