Computer tool can predict suicidal behaviour: Study

Using a person's spoken or written words, computer technology known as machine learning can identify with great accuracy whether that person is suicidal or not, a new study has found.

The study shows that machine learning is up to 93 per cent accurate in correctly classifying a suicidal person and 85 per cent accurate in identifying a person who is suicidal, has a mental illness but is not suicidal, or neither.

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 John Pestian, professor at Cincinnati Children's Hospital Medical Centre in the US and the study's lead author.

"These computational approaches provide novel opportunities to apply technological innovations in suicide care and prevention, and it surely is needed," said Pestian.

"When you look around health care facilities, you see tremendous support from technology, but not so much for those who care for mental illness.

"Only now are our algorithms capable of supporting those caregivers. This methodology easily can be extended to schools, shelters, youth clubs, juvenile justice centres and community centres, where earlier identification may help to reduce suicide attempts and deaths," he said.

Pestian and his colleagues enrolled 379 patients in the study between October 2013 and March 2015 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.

The scientists also noticed that the control patients tended to laugh more during interviews, sigh less and express less anger, less emotional pain and more hope.

The study was published in the journal Suicide and Life-Threatening Behaviour.