Applying Machine Learning to Linked Administrative and Clinical Data to Enhance the Detection of Homelessness among Vulnerable Veterans

U.S. military veterans who were discharged from service for misconduct are at high risk for homelessness. Stratifying homelessness risk based on both military service factors and clinical characteristics could facilitate targeted provision of preventive services to those at critical risk. Using administrative data from the Department of Defense and Veterans Health Administration for 25,821 misconduct-discharged Veterans, we developed a dataset that included demographic and clinical characteristics corresponding to 12-months, 3-months, and 1-month preceding the first documentation of homelessness (or a matched index encounter for those without homelessness). Clinical time-trend features were extracted and included as additional model inputs. We developed several random forest models to classify homelessness risk. Models based on 1- and 3-months of data performed roughly as well as those based on 12-months of data. In best-performing models, 70% of those identified as at high-risk became homeless; 30% identified as at moderate risk became homeless (AUC=0.80; recall=0.64, specificity=0.82). Findings suggest the viability of risk stratification for targeting resources.