Translating promise into practice: a review of machine learning in suicide research and prevention.

心理干预 稀缺 透明度(行为) 自杀意念 人工智能 机器学习 医疗保健 可扩展性 计算机科学 心理学 医学
作者
Olivia J Kirtley,Kasper van Mens,Mark Hoogendoorn,Navneet Kapur,Derek de Beurs
出处
期刊:The Lancet Psychiatry [Elsevier]
卷期号:9 (3): 243-252
标识
DOI:10.1016/s2215-0366(21)00254-6
摘要

In ever more pressured health-care systems, technological solutions offering scalability of care and better resource targeting are appealing. Research on machine learning as a technique for identifying individuals at risk of suicidal ideation, suicide attempts, and death has grown rapidly. This research often places great emphasis on the promise of machine learning for preventing suicide, but overlooks the practical, clinical implementation issues that might preclude delivering on such a promise. In this Review, we synthesise the broad empirical and review literature on electronic health record-based machine learning in suicide research, and focus on matters of crucial importance for implementation of machine learning in clinical practice. The challenge of preventing statistically rare outcomes is well known; progress requires tackling data quality, transparency, and ethical issues. In the future, machine learning models might be explored as methods to enable targeting of interventions to specific individuals depending upon their level of need-ie, for precision medicine. Primarily, however, the promise of machine learning for suicide prevention is limited by the scarcity of high-quality scalable interventions available to individuals identified by machine learning as being at risk of suicide.
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