Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications

TL;DR

This paper reviews machine learning and clinical methods for detecting suicidal ideation, analyzing data sources, tasks, and datasets, and discusses current limitations and future research directions.

Contribution

It is the first comprehensive survey comparing machine learning and clinical approaches for suicidal ideation detection across various data sources.

Findings

Machine learning methods utilize social media content for automatic detection.

Clinical methods rely on expert interaction and questionnaires.

Several datasets and tasks are identified for future research.

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 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.