TY - JOUR
T1 - A Machine Learning Approach to Predicting New-onset Depression in a Military Population
AU - Sampson, Laura
AU - Jiang, Tammy
AU - Gradus, Jaimie L.
AU - Cabral, Howard J.
AU - Rosellini, Anthony J.
AU - Calabrese, Joseph R.
AU - Cohen, Gregory H.
AU - Fink, David S.
AU - King, Anthony P.
AU - Liberzon, Israel
AU - Galea, Sandro
N1 - Publisher Copyright:
© 2021 The Authors. Psychiatric Research and Clinical Practice published by Wiley Periodicals LLC. on behalf of the American Psychiatric Association
PY - 2021/9/1
Y1 - 2021/9/1
N2 - Objective: Depression is one of the most common mental disorders in the United States in both civilian and military populations, but few prospective studies assess a wide range of predictors across multiple domains for new-onset (incident) depression in adulthood. Supervised machine learning methods can identify predictors of incident depression out of many different candidate variables, without some of the assumptions and constraints that underlie traditional regression analyses. The objectives of this study were to identify predictors of incident depression across 5 years of follow-up using machine learning, and to assess prediction accuracy of the algorithms. Methods: Data were from a cohort of Army National Guard members free of history of depression at baseline (n = 1951 men and 298 women), interviewed once per year for probable depression. Classification trees and random forests were constructed and cross-validated, using 84 candidate predictors from the baseline interviews. Results: Stressors and traumas such as emotional mistreatment and adverse childhood experiences, demographics such as being a parent or student, and military characteristics including paygrade and deployment location were predictive of probable depression. Cross-validated random forest algorithms were moderately accurate (68% for women and 73% for men). Conclusions: Events and characteristics throughout the life course, both in and outside of deployment, predict incident depression in adulthood among military personnel. Although replication studies are needed, these results may help inform potential intervention targets to reduce depression incidence among military personnel. Future research should further refine and explore interactions between identified variables.
AB - Objective: Depression is one of the most common mental disorders in the United States in both civilian and military populations, but few prospective studies assess a wide range of predictors across multiple domains for new-onset (incident) depression in adulthood. Supervised machine learning methods can identify predictors of incident depression out of many different candidate variables, without some of the assumptions and constraints that underlie traditional regression analyses. The objectives of this study were to identify predictors of incident depression across 5 years of follow-up using machine learning, and to assess prediction accuracy of the algorithms. Methods: Data were from a cohort of Army National Guard members free of history of depression at baseline (n = 1951 men and 298 women), interviewed once per year for probable depression. Classification trees and random forests were constructed and cross-validated, using 84 candidate predictors from the baseline interviews. Results: Stressors and traumas such as emotional mistreatment and adverse childhood experiences, demographics such as being a parent or student, and military characteristics including paygrade and deployment location were predictive of probable depression. Cross-validated random forest algorithms were moderately accurate (68% for women and 73% for men). Conclusions: Events and characteristics throughout the life course, both in and outside of deployment, predict incident depression in adulthood among military personnel. Although replication studies are needed, these results may help inform potential intervention targets to reduce depression incidence among military personnel. Future research should further refine and explore interactions between identified variables.
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U2 - 10.1176/appi.prcp.20200031
DO - 10.1176/appi.prcp.20200031
M3 - Article
AN - SCOPUS:85114193646
SN - 2575-5609
VL - 3
SP - 115
EP - 122
JO - Psychiatric Research and Clinical Practice
JF - Psychiatric Research and Clinical Practice
IS - 3
ER -