Deep learning prediction models based on EHR trajectories: A systematic review.

Journal: Journal of biomedical informatics
Published Date: Jun 26, 2023

Abstract

BACKGROUND: Electronic health records (EHRs) are generated at an ever-increasing rate. EHR trajectories, the temporal aspect of health records, facilitate predicting patients' future health-related risks. It enables healthcare systems to increase the quality of care through early identification and primary prevention. Deep learning techniques have shown great capacity for analyzing complex data and have been successful for prediction tasks using complex EHR trajectories. This systematic review aims to analyze recent studies to identify challenges, knowledge gaps, and ongoing research directions.

Authors

  • Ali Amirahmadi
    Center for Applied Intelligent Systems Research, Halmstad University, Sweden.
  • Mattias Ohlsson
    Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden.
  • Kobra Etminani
    Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran. Electronic address: EtminaniK@mums.ac.ir.

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