Machine learning can identify suicidal patients

CINCINNATI, Nov. 7 (UPI) — Scientists say machine learning is up to 93 percent accurate in identifying a suicidal person based on their responses to interview questions. The algorithm was described in a study published in the journal Suicide and Life-Threatening Behavior. Researchers were able to use the tool to classify patients as being suicidal, mentally ill but not suicidal, or neither. “These computational approaches provide novel opportunities to apply technological innovations in suicide care and prevention, and it surely is needed,” study author John Pestian said in a press release. “When you look around healthcare 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.” In the study, researchers recruited 379…

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