Researchers including those from Florida State University in the US accessed a massive data repository containing the electronic health records of about two million patients.
The team then combed through the electronic health records, which were anonymous, and identified more than 3,200 people who had attempted suicide.
Using machine learning to examine all of those details, the algorithms were able to 'learn' which combination of factors in the records could most accurately predict future suicide attempts.
"This kind of work lets us apply algorithms that can consider hundreds of data points in someone's medical record and potentially reduce them to clinically meaningful information," said Ribeiro.
The study offers a fascinating finding that machine learning - a future frontier for artificial intelligence can predict with 80-90 per cent accuracy of whether someone will attempt suicide as far off as two years into the future.
For example, the accuracy climbs to 92 per cent one week before a suicide attempt when artificial intelligence focuses on general hospital patients.
"This study provides evidence that we can predict suicide attempts accurately. We can predict them accurately over time, but we are best at predicting them closer to the event.
"We also know, based on this study, that risk factors like how they work and how important they are also change over time." Ribeiro said.
The study appears in the journal Clinical Psychological Science.
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