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Machine learning algorithm helps predicts suicide risk

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A machine learning algorithm that predicts suicide attempt recently underwent a prospective trial at the institution where it was developed, Vanderbilt University Medical Center.

Over the 11 consecutive months concluding in April 2020, predictions ran silently in the background as adult patients were seen at VUMC. The algorithm, dubbed the Vanderbilt Suicide Attempt and Ideation Likelihood (VSAIL) model, uses routine information from electronic health records (EHRs) to calculate 30-day risk of return visits for suicide attempt, and, by extension, suicidal ideation.

Suicide has been on the rise in the U.S. for a generation and is estimated to claim the lives of 14 in 100,000 Americans each year, making it the nation’s tenth leading cause of death. Nationally, some 8.5% of suicide attempts end in death.

Colin Walsh, MD, MA, and colleagues evaluated the performance of the predictive algorithm with an eye to its potential clinical implementation. They reported the study in JAMA Network Open.

Upon stratifying adult patients into eight groups according to their risk scores per the algorithm, the top stratum alone accounted for more than one-third of all suicide attempts documented in the study, and approximately half of all cases of suicidal ideation. As documented in the EHR, one in 23 individuals in this high-risk group went on to report suicidal thoughts, and one in 271 went on to attempt suicide.

Today across the Medical Center, we cannot screen every patient for suicide risk in every encounter — nor should we. But we know some individuals are never screened despite factors that might put them at higher risk. This risk model is a first pass at that screening and might suggest which patients to screen further in settings where suicidality is not often discussed.”

Colin Walsh, Assistant Professor, Biomedical Informatics, Medicine and Psychiatry
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