Biomarkers and computational analysis: The future of suicide prevention

As the rates of suicide deaths continue to climb in the U.S., scientists are examining patterns of brain activity detected on MRI scans, levels of stress hormones found in the blood, computational algorithms and sleeping habit assessments as possible ways to detect suicide risk, according to The Wall Street Journal.

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The need for such tools is a serious one. A study conducted by Harvard University in Cambridge, Mass., and Boston’s Massachusetts General Hospital found that clinician assessment was no more accurate than chance in predicting which patients visiting a psychiatric emergency room were likely to attempt suicide in the next six months.

According to an April report from the CDC, the rates of suicide death increased by 24 percent from 1999 to 2014. Matthew K. Nock, PhD, a psychology professor at Harvard University and one of the nation’s top suicide researchers, told WSJ, “It is a leading cause of death and we just don’t have a handle on it.”

Dr. Nock and colleagues are working to conclude a study involving the medical records of 1.7 million people. For the study, computers used 30,000 measures applied to patient data to determine suicide risk. On average, the computational analysis successfully detected 45 percent of suicidal acts approximately three years prior to the event.

Find more information about suicide in America here.

More articles on population health: 
Atlantic Health System funding $325k to improve health of communities 
Study: Pop stars endorse unhealthy food 
American death rate rises for first time in decade: 7 things to know

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