首页|Research Division Reports Findings in Machine Learning (Late-life suicide: machi ne learning predictors from a large European longitudinal cohort)
Research Division Reports Findings in Machine Learning (Late-life suicide: machi ne learning predictors from a large European longitudinal cohort)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting out of Padova, Italy, by News Rx editors, research stated, “People in late adulthood die by suicide at the hig hest rate worldwide. However, there are still no tools to help predict the risk of death from suicide in old age.” Our news journalists obtained a quote from the research from Research Division, “Here, we leveraged the Survey of Health, Ageing, and Retirement in Europe (SHAR E) prospective dataset to train and test a machine learning model to identify pr edictors for suicide in late life. Of more than 16,000 deaths recorded, 74 were suicides. We matched 73 individuals who died by suicide with people who died by accident, according to sex (28.8% female in the total sample), age at death (67 ? 16.4 years), suicidal ideation (measured with the EURO-D scale), and the number of chronic illnesses. A random forest algorithm was trained on d emographic data, physical health, depression, and cognitive functioning to extra ct essential variables for predicting death from suicide and then tested on the test set. The random forest algorithm had an accuracy of 79% (95% CI 0.60-0.92, p = 0.002), a sensitivity of.80, and a specificity of.78. Among th e variables contributing to the model performance, the three most important fact ors were how long the participant was ill before death, the frequency of contact with the next of kin and the number of offspring still alive. Prospective clini cal and social information can predict death from suicide with good accuracy in late adulthood.”
PadovaItalyEuropeCyborgsEmerging TechnologiesHealth and MedicineMachine LearningMental HealthRisk and Pr eventionSuicide
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