Applying machine learning to understand factors predicting pharmacotherapy for mental health support among adults with intellectual and developmental disabilities.
Matthew Bogenschutz, Michael Broda, Parthenia Dinora, Seb Prohn, Sarah Lineberry, Angela West
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引用次数: 0
Abstract
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.
期刊介绍：
Social Work in Mental Health is an exciting contribution from the editors of our highly respected journal, Social Work in Health Care. This journal offers quality articles on clinical practice, education, research, collaborative relationships, mental health policy, and the delivery of mental health care services. This scholarly, creative, and lively journal presents material valuable to social workers in all sectors of mental health care. It is devoted to social work theory, practice, and administration in a wide variety of mental health care settings. Social Work in Mental Health will help you improve your practice in these areas, demonstrating the vital role of social services in mental health care delivery systems.
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