Applying machine learning to understand factors predicting pharmacotherapy for mental health support among adults with intellectual and developmental disabilities

Americans with intellectual and developmental disabilities (IDD) experience health disparities, including in their mental health. This often leads to disproportionate use of psychotropic medications, sometimes leading to serious side effects. We used machine learning to analyze an integrated dataset (years 2018-2022) from one U.S. state with 2907 observations and 850 variables to determine what factors were most predictive of pharmacotherapy use to support mental health needs among people with IDD. Our algorithm performed strongly, with the presence of mood, anxiety, and psychotic disorders, documented behavioral support needs, and overall support needs all contributing strongly to the algorithm's accuracy. Implications for social workers and other mental health professionals are discussed.