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山东大学学报 (医学版) ›› 2022, Vol. 60 ›› Issue (4): 10-16.doi: 10.6040/j.issn.1671-7554.0.2021.0175

• 专家综述 • 上一篇    下一篇

机器学习用于自杀研究的综述

况利1,*(),徐小明1,曾琪2   

  1. 1. 重庆医科大学附属第一医院精神科, 重庆 400016
    2. 重庆医科大学附属大学城医院心理卫生中心, 重庆 401331
  • 收稿日期:2021-02-09 出版日期:2022-04-10 发布日期:2022-04-22
  • 通讯作者: 况利 E-mail:kuangli0308@163.com
  • 作者简介:况利，教授，博士研究生导师，重庆医科大学精神医学系主任, 重庆医科大学附属大学城医院心理卫生中心主任。主要研究方向为青少年心理卫生、焦虑抑郁和自杀、突发事件危机干预。以第一作者和通讯作者发表国内外核心期刊文章百余篇，参编国家级教材10余部，主持科研课题30余项。目前为教育部高等学校精神医学专业教学指导委员会委员，中华医学会心身医学分会副主任委员，中华医学会精神医学会及行为医学分会常务委员，中国心理卫生协会心身专委会常务委员，重庆市心理健康研究中心主任，西部精神医学协会副会长，重庆市医学会精神病学专委会主任委员，重庆医院协会精神卫生防治机构管理分会会长，重庆法医精神病司法鉴定专委会主任委员，2016年获聘“中华医学会精神卫生科普专家”，2018年获得“中国心身医学特殊贡献奖”，2019年获得“重庆市第三批学术技术带头人”称号，2021年获得重庆英才计划“重庆英才·名家名师”称号
  • 基金资助:
    国家自然科学基金(81671360);国家自然科学基金(81971286);重庆市自然科学基金(cstc2018jcyjAX0164);重庆市科卫联合医学科研项目(2018QNXM014);重庆市社会事业与民生保障科技创新专项重点研发项目(cstc2017shms-zdyfX0038)

Review of machine learning used in the field of suicide

Li KUANG1,*(),Xiaoming XU1,Qi ZENG2   

  1. 1. Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
    2. Mental Health Center, University-Town Hospital of Chongqing Medical University, Chongqing 401331, China
  • Received:2021-02-09 Online:2022-04-10 Published:2022-04-22
  • Contact: Li KUANG E-mail:kuangli0308@163.com

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摘要：

全球每年有80万人死于自杀，自杀未遂数量大约是其20倍。自杀不仅是严重的公共卫生事件，而且对自杀者周围的人产生重大而深远的影响。更准确、便捷、及时地预测自杀行为一直是研究者的目标。论文对近五年应用于自杀意念与行为的机器学习研究进行回顾，分析机器学习用于自杀研究的有效性、可行性，对机器学习应用于自杀领域的研究提出建议，为未来的研究提供方向。

关键词: 机器学习, 自杀, 自杀未遂, 自杀意念, 大数据

Abstract:

A total of 800, 000 people die of suicide every year over the world, and the number of attempted suicides is about 20 times number of suicide. Suicide not only is a serious public health event, but also significantly and far-reachingly impact on people around suicides. More accurate, convenient, and timely prediction of suicidal behavior has always been the goal of researchers. This study summarized the researches on machine learning applied to suicidal ideation and behavior in the past 5 years, analyzed the effectiveness and feasibility of machine learning for suicide research, made recommendations, and provided a direction for future research.

Key words: Machine learning, Suicide, Suicide attempt, Suicidal ideation, Big data

中图分类号: 

  • R774
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