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When people reach out during a mental health crisis, a top priority for counselors is identifying those at high risk of suicide. The distressed person's language holds critical clues, and a new tool developed by scientists at MIT's McGovern Institute for Brain Research is designed to pick up on and rapidly evaluate those signals.

The language-processing tool was developed by Daniel Low, a former graduate student in senior research scientist Satrajit Ghosh's Senseable Intelligence Group. Low is now a research scientist at the Child Mind Institute, where he leads its AI, Risk, and Contemplative Science Lab, and a visiting scholar at Harvard University.

The tool uses a custom-built list of words and phrases linked to 49 suicide risk factors, searching text for those signals and using them to estimate an individual's risk.

Ghosh, Low and colleagues report in the Journal of Psychopathology and Clinical Science that their tool accurately predicts suicide risk from text conversations with crisis counselors.

It is already helping to clarify which suicide risk factors matter most in times of crisis. With more validation, it could help with risk assessment in clinical settings and crisis-support situations.

Suicide attempts are notoriously difficult to predict. Dozens of risk factors have been linked to suicide, and even trained clinicians struggle to identify who will make an attempt among those who have some form of suicidal ideation.

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