首页|Data on Machine Learning Reported by Jack Tsai and Colleagues (Predicting homelessness among transitioning U.S. Army soldiers)
Data on Machine Learning Reported by Jack Tsai and Colleagues (Predicting homelessness among transitioning U.S. Army soldiers)
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New research on Machine Learning is the subject of a report. According to news reporting originating from Washington, District of Columbia, by NewsRx correspondents, research stated, "This study develops a practical method to triage Army transitioning service members (TSMs) at highest risk of homelessness to target a preventive intervention. The sample included 4,790 soldiers from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS) who participated in one of three Army STARRS 2011-2014 baseline surveys followed by the third wave of the STARRS-LS online panel surveys (2020-2022)." Our news editors obtained a quote from the research, "Two machine learning models were trained: a Stage-1 model that used administrative predictors and geospatial data available for all TSMs at discharge to identify high-risk TSMs for initial outreach; and a Stage-2 model estimated in the high-risk subsample that used self-reported survey data to help determine highest risk based on additional information collected from high-risk TSMs once they are contacted. The outcome in both models was homelessness within 12 months after leaving active service. Twelve-month prevalence of post-transition homelessness was 5.0% (SE=0.5). The Stage-1 model identified 30% of high-risk TSMs who accounted for 52% of homelessness. The Stage-2 model identified 10% of all TSMs (i.e., 33% of high-risk TSMs) who accounted for 35% of all homelessness (i.e., 63% of the homeless among high-risk TSMs)."
WashingtonDistrict of ColumbiaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningRisk and Prevention
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