A study published in JAMA Psychiatry outlined sex-specific suicide prediction models by using a novel machine learning design. Lead study author Jaimie L. Gradus, DMSc, DSc, of Boston University School of Public Health, Massachusetts, and colleagues used machine learning to analyze data from Danish single-payer healthcare and social registries from 1995 through 2015. As such, the source population for the case cohort study comprised all people living in Denmark since 1995. The main outcome was death from suicide, and the study included 1339 variables as exposures. The researchers created a comparison sub-cohort comprised of a 5% random sample of registry data.