A machine learning approach to identify distinct subgroups of veterans at risk for hospitalization or death using administrative and electronic health record data.

High-risk Veterans are a heterogeneous population consisting of multiple distinct subgroups-many of which are not defined by clinical comorbidities-with distinct utilization and outcome patterns. To our knowledge, this represents the largest application of ML clustering methods to subgroup a high-risk population. Further study is needed to determine whether distinct subgroups may benefit from individualized interventions.