Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records.

Journal: Translational psychiatry
PMID: 32080165

Abstract

Suicide is the tenth leading cause of death in the United States (US). An early-warning system (EWS) for suicide attempt could prove valuable for identifying those at risk of suicide attempts, and analyzing the contribution of repeated attempts to the risk of eventual death by suicide. In this study we sought to develop an EWS for high-risk suicide attempt patients through the development of a population-based risk stratification surveillance system. Advanced machine-learning algorithms and deep neural networks were utilized to build models with the data from electronic health records (EHRs). A final risk score was calculated for each individual and calibrated to indicate the probability of a suicide attempt in the following 1-year time period. Risk scores were subjected to individual-level analysis in order to aid in the interpretation of the results for health-care providers managing the at-risk cohorts. The 1-year suicide attempt risk model attained an area under the curve (AUC ROC) of 0.792 and 0.769 in the retrospective and prospective cohorts, respectively. The suicide attempt rate in the "very high risk" category was 60 times greater than the population baseline when tested in the prospective cohorts. Mental health disorders including depression, bipolar disorders and anxiety, along with substance abuse, impulse control disorders, clinical utilization indicators, and socioeconomic determinants were recognized as significant features associated with incident suicide attempt.

Authors

  • Le Zheng
    Departments of Surgery, Stanford University, Stanford, CA 94305, USA.
  • Oliver Wang
    HBI Solutions Inc, Palo Alto, CA, United States.
  • Shiying Hao
    Departments of Surgery, Stanford University, Stanford, CA 94305, USA.
  • Chengyin Ye
    Department of Health Management, Hangzhou Normal University, Hangzhou, China.
  • Modi Liu
    HBI Solutions Inc, Palo Alto, CA, United States.
  • Minjie Xia
    HBI Solutions Inc, Palo Alto, CA, United States.
  • Alex N Sabo
    Department of Psychiatry and Behavioral Sciences, Berkshire Medical Center, Pittsfield, MA, USA.
  • Liliana Markovic
    Department of Psychiatry and Behavioral Sciences, Berkshire Medical Center, Pittsfield, MA, USA.
  • Frank Stearns
    HBISolutions Inc., Palo Alto, CA 94301, USA.
  • Laura Kanov
    HBI Solutions Inc, Palo Alto, CA, USA.
  • Karl G Sylvester
    Departments of Surgery, Stanford University, Stanford, CA 94305, USA.
  • Eric Widen
    HBISolutions Inc., Palo Alto, CA 94301, USA.
  • Doff B McElhinney
    Stanford University, Stanford, California, United States of America.
  • Wei Zhang
    The First Affiliated Hospital of Nanchang University, Nanchang, China.
  • Jiayu Liao
    Department of Bioengineering, Bourns College of Engineering, University of California at Riverside, Riverside, CA, USA.
  • Xuefeng B Ling
    Departments of Surgery, Stanford University, Stanford, CA 94305, USA. Electronic address: bxling@stanford.edu.

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