Posted: Oct 28, 2022
Can Smartphones Help Predict Suicide?
E Barry, NY Times, September 30, 2022
In the field of mental health, few new areas generate as much excitement as machine learning, which uses computer algorithms to better predict human behavior. There is, at the same time, exploding interest in biosensors that can track a person’s mood in real time, factoring in music choices, social media posts, facial expression and vocal expression. A unique research project is tracking hundreds of people at risk for suicide, using data from smartphones and wearable biosensors to identify periods of high danger — and intervene.
Machine Learning Analysis of Handgun Transactions to Predict Firearm Suicide Risk.
Laqueur Hannah S et al. JAMA network open 2022 7 (7) e2221041
Can handgun purchasing records, coupled with machine learning techniques, be used to forecast firearm suicide risk?
In this prognostic study of nearly 2 million individuals with handgun transaction records, among transactions classified in the riskiest 5%, close to 40% were associated with a purchaser who died by firearm suicide within 1 year. Among the small number of transactions with a random forest score of 0.95 and above, more than two-thirds were affiliated with a purchaser who died by firearm suicide within 1 year (24 of 35).
Association of Genome-Wide Polygenic Scores for Multiple Psychiatric and Common Traits in Preadolescent Youths at Risk of Suicide
YY Joo et al, JAMA Network Open, February 21, 2022
Are genome-wide polygenic scores for specific psychiatric and common traits associated with high risk of suicide among preadolescent youths? In this cohort study of 11?869 preadolescent youths in the US, multiple genome-wide polygenic scores were significantly associated with suicidal thoughts and behaviors (ideation or attempts); specific genome-wide polygenic scores associated with the risk of suicide included attention-deficit/hyperactivity disorder, general happiness, and posttraumatic stress disorder.