Predicting Optimal Hypertension Treatment Pathways Using Recurrent Neural Networks.

Journal: International journal of medical informatics
Published Date: Mar 21, 2020

Abstract

BACKGROUND: In ambulatory care settings, physicians largely rely on clinical guidelines and guideline-based clinical decision support (CDS) systems to make decisions on hypertension treatment. However, current clinical evidence, which is the knowledge base of clinical guidelines, is insufficient to support definitive optimal treatment.

Authors

  • Xiangyang Ye
    School of Medicine, University of Utah, SLC, UT, USA.
  • Qing T Zeng
    George Washington University, 800 22nd St. NW, Science and Engineering Hall, Ste. #8390, Washington, DC, 20052, USA.
  • Julio C Facelli
    Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
  • Diana I Brixner
    Department of Pharmacotherapy, The University of Utah, 30 South 2000 East, Salt Lake City, UT, 84108, USA.
  • Mike Conway
    Department of Biomedical Informatics, School of Medicine University of Utah 421 Wakara Way Ste 140, Salt Lake City, UT 84108-3514, USA.
  • Bruce E Bray
    Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.

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