The researchers, led by Jessica Ribeiro, PhD, identified the EHRs of 2 million Tennessee patients, more than 3,200 of whom had attempted suicide. The researchers used machine learning on these patients’ medical histories to determine which combination of risk factors most accurately predicted future suicide attempts.
The machine learning algorithm could predict suicide attempts with between 80 percent and 90 percent accuracy as far as two years into the future. The algorithm’s accuracy increased based on closeness to the time of the suicide attempt; accuracy was as high as 92 percent when identifying general hospital patients at risk for a suicide attempt within one week.
Dr. Ribeiro conducted this project in collaboration with psychology researcher Joseph Franklin, PhD, of Florida State University and biomedical informatics researcher Colin Walsh, MD, of Vanderbilt University Medical Center in Nashville, Tenn. Their findings will be published in Clinical Psychological Science.
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