AI Identifies Suicidal Behavior With 93 Percent Accuracy
According to a new study, computers can identify with surprising accuracy humans who feel suicidal.
A new study shows that computer technology known as machine learning is up to 93 percent accurate in correctly classifying a suicidal person and 85 percent 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 behavior, says John Pestian, PhD, professor in the divisions of Biomedical Informatics and Psychiatry at Cincinnati Children's Hospital Medical Center 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," says Dr. 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 centers, and community centers, where earlier identification may help to reduce suicide attempts and deaths."
Science fiction readers have had plenty of sessions with computer therapists; consider the Sigfrid von Shrink from Frederik Pohl's 1970 novel Gateway and of course Dr. Smile from Philip K. Dick's 1965 novel The Three Stigmata of Palmer Eldritch.
Pride of place for the earliest reference that I know about goes to the machine psychologists from James Blish's 1957 novel Cities in Flight.
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