Citation

Ji S, Pan S, Li X, Cambria E, Long G, Huang Z. IEEE Trans. Comput. Soc. Syst. 2021; 8(1): 214-226.

Copyright

(Copyright © 2021, Institute of Electrical and Electronics Engineers, Inc.)

DOI

10.1109/TCSS.2020.3021467

PMID

unavailable

Abstract

Suicide is a critical issue in modern society. Early detection and prevention of suicide attempts should be addressed to save people's life. Current suicidal ideation detection (SID) methods include clinical methods based on the interaction between social workers or experts and the targeted individuals and machine learning techniques with feature engineering or deep learning for automatic detection based on online social contents. This article is the first survey that comprehensively introduces and discusses the methods from these categories. Domain-specific applications of SID are reviewed according to their data sources, i.e., questionnaires, electronic health records, suicide notes, and online user content. Several specific tasks and data sets are introduced and summarized to facilitate further research. Finally, we summarize the limitations of current work and provide an outlook of further research directions. © 2014 IEEE.

Language: en

Keywords

Suicidal ideation; Surveys; Learning systems; Engineering research; Electronic health record; Deep learning; Automatic Detection; Clinical methods; Domain-specific application; feature engineering; Feature engineerings; Machine learning methods; Machine learning techniques; social content; suicidal ideation detection (SID)