An Israeli research team has combined genetics and artificial intelligence to detect suicidal tendencies in bipolar patients with near-perfect precision, offering new hope for early intervention.
Until now, suicide-risk assessments have depended almost entirely on self-reports and behavioral observation. As a result, suicide remains one of the leading causes of premature death among people with bipolar disorder — with up to 40 percent attempting it at least once in their lives.
Led by Professor Shani Stern of the University of Haifa’s Sagol Department of Neurobiology, researchers examined samples from bipolar patients, including individuals who later died by suicide or had prior attempts. By comparing gene expression in white blood cells, they identified distinctive molecular signatures that distinguished high-risk patients from others.
Genes linked to immune function, ion-channel signaling, and neural connectivity showed the strongest correlation with suicide vulnerability. Using artificial intelligence, the team built a predictive model capable of determining suicide risk with more than 95 percent accuracy.
“Being able to identify those at risk early would allow for early intervention and save lives,” said Stern. “This is just the first step toward a tool that integrates genetic, brain, and behavioral data to prevent tragedies.”
Published this month in Translational Psychiatry, the study was conducted in partnership with scientists from Canada and Italy. With further validation, the test could enable periodic monitoring of vulnerable patients and help psychiatrists adjust treatment plans or supervision levels before a crisis emerges.
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