Suicide Risk Assessment Using Machine Learning and Social Networks: a Scoping Review.

ABSTRACT: 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 inform

SUBMITTER: Castillo-Sanchez G 

PROVIDER: S-EPMC7649702 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature