Identifying momentary suicidal ideation using machine learning in patients at high-risk for suicide.

Journal: Journal of affective disorders
PMID: 39142570

Abstract

BACKGROUND: Strategies to detect the presence of suicidal ideation (SI) or characteristics of ideation that indicate marked suicide risk are critically needed to guide interventions and improve care during care transition periods. Some studies indicate that machine learning can be applied to momentary data to improve classification of SI. This study examined whether the classification accuracy of these models varies as a function of type of training data or characteristics of ideation.

Authors

  • M L Bozzay
    Department of Psychiatry & Human Behavior, Alpert Medical School of Brown University, Box G-BH, Providence, RI 02912, United States; Department of Psychiatry and Behavioral Health, The Ohio State University Wexner Medical Center, 370 W. 9th Avenue, Columbus, OH 43210, United States. Electronic address: Melanie.Bozzay@osumc.edu.
  • C D Hughes
    Department of Psychiatry & Human Behavior, Alpert Medical School of Brown University, Box G-BH, Providence, RI 02912, United States; Department of Psychosocial Research, Butler Hospital, 345 Blackstone Blvd., Providence, RI 02906, United States.
  • C Eickhoff
    School of Medicine, University of Tübingen, Schaffhausenstr. 77, 72072 Tübingen, Germany.
  • H Schatten
    Department of Psychiatry & Human Behavior, Alpert Medical School of Brown University, Box G-BH, Providence, RI 02912, United States; Department of Psychosocial Research, Butler Hospital, 345 Blackstone Blvd., Providence, RI 02906, United States.
  • M F Armey
    Department of Psychiatry & Human Behavior, Alpert Medical School of Brown University, Box G-BH, Providence, RI 02912, United States; Department of Psychosocial Research, Butler Hospital, 345 Blackstone Blvd., Providence, RI 02906, United States.

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