Suicide Risk Assessment Using Machine Learning and Social Networks: a Scoping Review.
Suicide Risk Assessment Using Machine Learning and Social Networks: a Scoping Review.
复制标题
使用机器学习和社会网络的自杀风险评估：范围审查。
DOI：
10.1007/s10916-020-01669-5
复制
发表时间：
2020-11-09
影响因子：
5.3
通讯作者：
De la Torre-Díez I
中科院分区：
文献类型：
--
作者：
Castillo-Sánchez G;Marques G;Dorronzoro E;Rivera-Romero O;Franco-Martín M;De la Torre-Díez I
根据世界卫生组织(WHO)2016年的报告，大约80万人自杀。此外，自杀是15岁至29岁人群非自然死亡的第二大原因。本文综述了关于使用机器学习方法在社交网络上进行自杀检测的文献的最新进展。在此基础上，分析了社交网络自杀检测的目标、数据收集技术、开发过程和验证指标。作者使用Arksey和O‘Malley等人提出的方法进行了范围审查。采用PRISMA方案选择相关研究。这项范围界定审查旨在确定用于根据社交网络上发布的信息预测自杀风险的机器学习技术。使用的数据库是PubMed、Science Direct、IEEE Xplore和Web of Science。总体而言，50%的纳入研究(8/16)明确报告了使用数据挖掘技术进行特征提取、特征检测或实体识别。最常报道的方法是语言查询和字数统计(4/8，50%)，其次是潜在狄利克雷分析，潜在语义分析，和单词2vec(2/8，25%)。只有一项纳入研究使用了非负矩阵因式分解和主成分分析(12.5%)。总体而言，8篇研究论文中有3篇(37.5%)结合了其中一种以上的技术。在纳入的16项研究中，有10项实现了支持向量机(62.5%)。最后，75%的分析研究使用Python实现了基于机器学习的模型。网上版载有补充材料，可在10.1007/s10916-020-01669-5查阅。
According to the World Health Organization (WHO) report in 2016, around 800,000 of individuals have committed suicide. Moreover, suicide is the second cause of unnatural death in people between 15 and 29 years. This paper reviews state of the art on the literature concerning the use of machine learning methods for suicide detection on social networks. Consequently, the objectives, data collection techniques, development process and the validation metrics used for suicide detection on social networks are analyzed. The authors conducted a scoping review using the methodology proposed by Arksey and O’Malley et al. and the PRISMA protocol was adopted to select the relevant studies. This scoping review aims to identify the machine learning techniques used to predict suicide risk based on information posted on social networks. The databases used are PubMed, Science Direct, IEEE Xplore and Web of Science. In total, 50% of the included studies (8/16) report explicitly the use of data mining techniques for feature extraction, feature detection or entity identification. The most commonly reported method was the Linguistic Inquiry and Word Count (4/8, 50%), followed by Latent Dirichlet Analysis, Latent Semantic Analysis, and Word2vec (2/8, 25%). Non-negative Matrix Factorization and Principal Component Analysis were used only in one of the included studies (12.5%). In total, 3 out of 8 research papers (37.5%) combined more than one of those techniques. Supported Vector Machine was implemented in 10 out of the 16 included studies (62.5%). Finally, 75% of the analyzed studies implement machine learning-based models using Python. The online version contains supplementary material available at 10.1007/s10916-020-01669-5.
登录
查看更多内容
影响因子：
9.9
作者：
Cavazos-Rehg PA;Krauss MJ;Sowles S;Connolly S;Rosas C;Bharadwaj M;Bierut LJ
通讯作者：
Bierut LJ
影响因子：
--
作者：
Denecke, Kerstin;Gabarron, Elia;Merolli, Mark
通讯作者：
Merolli, Mark
影响因子：
7.4
作者：
Jung, Hyesil;Park, Hyeoun-Ae;Song, Tae-Min
通讯作者：
Song, Tae-Min
DOI：
10.1016/j.ijinfomgt.2017.08.003
发表时间：
2018-02-01
影响因子：
21
作者：
Kim, Jooho;Hastak, Makarand
通讯作者：
Hastak, Makarand
影响因子：
10.5
作者：
Cheng, ATA;Chen, THH;Jenkins, R
通讯作者：
Jenkins, R