Enhancing Patient Outcome Prediction Through Deep Learning With Sequential Diagnosis Codes From Structured Electronic Health Record Data: Systematic Review.

Journal: Journal of medical Internet research
Published Date: Mar 18, 2025

Abstract

BACKGROUND: The use of structured electronic health records in health care systems has grown rapidly. These systems collect huge amounts of patient information, including diagnosis codes representing temporal medical history. Sequential diagnostic information has proven valuable for predicting patient outcomes. However, the extent to which these types of data have been incorporated into deep learning (DL) models has not been examined.

Authors

  • Tuankasfee Hama
    Institute of Health Informatics, University College London, London, UK.
  • Mohanad M Alsaleh
    Institute of Health Informatics, University College London, London, UK; Department of Health Informatics, College of Public Health and Health Informatics, Qassim University, Al Bukayriyah, Saudi Arabia. Electronic address: mohanad.alsaleh.21@ucl.ac.uk.
  • Freya Allery
    Institute of Health Informatics, University College London, London, UK.
  • Jung Won Choi
    Department of Neurosurgery, Brain Tumor Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
  • Christopher Tomlinson
    Institute of Health Informatics, University College London, London, United Kingdom.
  • Honghan Wu
    University College London, London, United Kingdom.
  • Alvina Lai
    Institute of Health Informatics, University College London, London, United Kingdom.
  • Nikolas Pontikos
    University College London Institute of Ophthalmology, London, UK n.pontikos@ucl.ac.uk.
  • Johan H Thygesen
    Division of Psychiatry, University College London, London, United Kingdom.

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