Using a person's spoken or written words, computer technology known as machine learning can help clinicians and caregivers identify with great accuracy whether that person is suicidal or not, according to a new study.
These results provide strong evidence for using advanced technology as a decision-support tool to help clinicians and caregivers identify and prevent suicidal behaviour, said the study's lead author John Pestian, Professor at Cincinnati Children's Hospital Medical Center in Ohio, US.
The study, published in the journal Suicide and Life-Threatening Behavior, showed that machine learning is up to 93 per cent accurate in correctly classifying a suicidal person.
For the study, Pestian and his colleagues enrolled 379 patients in the study from emergency departments and inpatient and outpatient centres at three sites.
Those enrolled included patients who were suicidal, were diagnosed as mentally ill and not suicidal, or neither -- serving as a control group.
Each patient completed standardised behavioural rating scales and participated in a semi-structured interview answering five open-ended questions to stimulate conversation, such as "Do you have hope?", "Are you angry?" and "Does it hurt emotionally?"
The researchers extracted and analysed verbal and non-verbal language from the data.
They then used machine learning algorithms to classify the patients into one of the three groups.
The results showed that machine learning algorithms can tell the differences between the groups with up to 93 per cent accuracy.
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