Semi-supervised Double Deep Learning Temporal Risk Prediction (SeDDLeR) with Electronic Health Records.

Journal: Journal of biomedical informatics
Published Date: Jul 14, 2024

Abstract

BACKGROUND: Risk prediction plays a crucial role in planning for prevention, monitoring, and treatment. Electronic Health Records (EHRs) offer an expansive repository of temporal medical data encompassing both risk factors and outcome indicators essential for effective risk prediction. However, challenges emerge due to the lack of readily available gold-standard outcomes and the complex effects of various risk factors. Compounding these challenges are the false positives in diagnosis codes, and formidable task of pinpointing the onset timing in annotations.

Authors

  • Isabelle-Emmanuella Nogues
    Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
  • Jun Wen
    School of Pharmacy, Second Military Medical University, Shanghai, 200433, China.
  • Yihan Zhao
    College of Veterinary Medicine, China Agricultural University, Beijing, China.
  • Clara-Lea Bonzel
    Department of Biomedical Informatics, Harvard Medical School, United States of America.
  • Victor M Castro
  • Yucong Lin
    Center for Statistical Science, Tsinghua University, Beijing, Beijing, China; Department of Industrial Engineering, Tsinghua University, Beijing, Beijing, China.
  • Shike Xu
    Department of Statistics, University of Connecticut, United States of America.
  • Jue Hou
    Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States.
  • Tianxi Cai
    Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States.

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