Graph-based clinical recommender: Predicting specialists procedure orders using graph representation learning.

Journal: Journal of biomedical informatics
Published Date: Jul 1, 2023

Abstract

OBJECTIVE: To determine whether graph neural network based models of electronic health records can predict specialty consultation care needs for endocrinology and hematology more accurately than the standard of care checklists and other conventional medical recommendation algorithms in the literature.

Authors

  • Sajjad Fouladvand
    Department of Computer Science, Institute for Biomedical Informatics, University of Kentucky, Lexington, KY, USA.
  • Federico Reyes Gomez
    Computer Science Department, Stanford University, Stanford, CA, United States of America. Electronic address: frg100@alumni.stanford.edu.
  • Hamed Nilforoshan
    Computer Science Department, Stanford University, Stanford, CA, United States of America. Electronic address: hamedn@cs.stanford.edu.
  • Matthew Schwede
    Biomedical Informatics Research, Stanford University, Stanford, CA, United States of America. Electronic address: mschwede@stanford.edu.
  • Morteza Noshad
    Stanford Center for Biomedical Informatics Research, Stanford, CA.
  • Olivia Jee
    Primary Care and Population Health, Stanford University, Stanford, CA, United States of America. Electronic address: ojee@stanford.edu.
  • Jiaxuan You
    Department of Computer Science, Stanford University.
  • Rok Sosic
    Computer Science Department, Stanford University, Stanford, CA, United States of America. Electronic address: rok@cs.stanford.edu.
  • Jure Leskovec
    Department of Computer Science, Stanford University.
  • Jonathan Chen
    Center for Biomedical Informatics Research, Stanford University, Stanford, California.

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