Acoustic Features for Identifying Suicide Risk in Crisis Hotline Callers: Machine Learning Approach.

Journal: Journal of medical Internet research
PMID: 40228243

Abstract

BACKGROUND: Crisis hotlines serve as a crucial avenue for the early identification of suicide risk, which is of paramount importance for suicide prevention and intervention. However, assessing the risk of callers in the crisis hotline context is constrained by factors such as lack of nonverbal communication cues, anonymity, time limits, and single-occasion intervention. Therefore, it is necessary to develop approaches, including acoustic features, for identifying the suicide risk among hotline callers early and quickly. Given the complicated features of sound, adopting artificial intelligence models to analyze callers' acoustic features is promising.

Authors

  • Zhengyuan Su
    Laboratory of Suicidal Behavior Research, Tianjin University, Tianjin, China.
  • Huadong Jiang
    Xi'an Mental Health Centre, Xi'an, China.
  • Ying Yang
    Department of Endocrinology, The Affiliated Hospital of Yunnan University, Kunming, China.
  • Xiangqing Hou
    Laboratory of Suicidal Behavior Research, Tianjin University, Tianjin, China.
  • Yanli Su
    Xi'an Mental Health Centre, Xi'an, China.
  • Li Yang
    Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.

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