GatorCLR: Personalized predictions of patient outcomes on electronic health records using self-supervised contrastive graph representation.

Journal: Journal of biomedical informatics
Published Date: Jun 2, 2025

Abstract

OBJECTIVE: Recently, there has been growing interest in analyzing large amounts of Electronic Health Record (EHR) data. Patient outcome prediction is a major area of interest in EHR analysis that focuses on predicting the future health status of patients using structured data types, such as diagnoses, medications, and procedures collected from longitudinal EHR data. We investigate and design self-supervised learning (SSL) paradigms to learn high-quality representations from longitudinal EHR data, aiming to effectively capture longitudinal relationships and patterns for improved patient outcome predictions.

Authors

  • Yuxi Liu
    Beijing Key Laboratory for Green Catalysis and Separation, Key Laboratory of Beijing on Regional Air Pollution Control, Key Laboratory of Advanced Functional Materials, Education Ministry of China, Laboratory of Catalysis Chemistry and Nanoscience, Department of Environmental Chemical Engineering, School of Environmental and Chemical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China.
  • Zhenhao Zhang
    Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America.
  • Jiacong Mi
    Faculty of Information Technology, Monash University, Clayton, 3800, VIC, Australia.
  • Shirui Pan
    Faculty of Information Technology, Monash University, Clayton, Australia.
  • Tianlong Chen
    Departments of Electrical and Computer Engineering & Computer Science and Engineering Texas A&M University, College Station, TX 77840.
  • Yi Guo
    Department of Respiratory and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
  • Xing He
    University of Florida, Gainesville, Florida, USA.
  • Jiang Bian
    Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, United States of America.

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