Prediction of multiclass surgical outcomes in glaucoma using multimodal deep learning based on free-text operative notes and structured EHR data.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Jan 18, 2024

Abstract

OBJECTIVE: Surgical outcome prediction is challenging but necessary for postoperative management. Current machine learning models utilize pre- and post-op data, excluding intraoperative information in surgical notes. Current models also usually predict binary outcomes even when surgeries have multiple outcomes that require different postoperative management. This study addresses these gaps by incorporating intraoperative information into multimodal models for multiclass glaucoma surgery outcome prediction.

Authors

  • Wei-Chun Lin
    School of Dental Technology, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan.
  • Aiyin Chen
    Ophthalmology Oregon Health & Science University, Portland, OR.
  • Xubo Song
    Knight Cancer Institute-CEDAR, Oregan Health & Science University, Portland, OR, United States.
  • Nicole G Weiskopf
    Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, 3181 S.W. Sam Jackson Park Rd, Portland, OR, 97239, United States.
  • Michael F Chiang
    National Eye Institute, National Institutes of Health, Bethesda, Maryland.
  • Michelle R Hribar
    Department of Ophthalmology, Casey Eye Institute, and.

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