Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications
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Suicide is a critical issue in the modern society. Early detection and prevention of suicide attempt 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 is the first survey that comprehensively introduces and discusses the methods from these categories. Domain-specific applications of suicidal ideation detection are also reviewed according to their data sources, i.e., questionnaires, electronic health records, suicide notes, and online user content. To facilitate further research, several specific tasks and datasets are introduced. Finally, we summarize the limitations of current work and provide an outlook of further research directions.
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