Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications
It addresses the critical problem of early suicide detection for public health by summarizing existing methods, but it is incremental as a review paper without new results.
This paper provides a comprehensive survey of machine learning methods for suicidal ideation detection, reviewing techniques from clinical interactions to automated approaches using various data sources like questionnaires and online content, and it introduces specific tasks and datasets to aid future research.
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 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 paper is the first survey that comprehensively introduces and discusses the methods from these categories. Domain-specific applications of suicidal ideation detection are reviewed according to their data sources, i.e., questionnaires, electronic health records, suicide notes, and online user content. Several specific tasks and datasets are introduced and summarized to facilitate further research. Finally, we summarize the limitations of current work and provide an outlook of further research directions.