title: Use of a machine learning framework to predict substance use disorder treatment success.
abstract: There are several methods for building prediction models. The wealth of currently available modeling techniques usually forces the researcher to judge, a priori, what will likely be the best method. Super learning (SL) is a methodology that facilitates this decision by combining all identified prediction algorithms pertinent for a particular prediction problem. SL generates a final model that is at least as good as any of the other models considered for predicting the outcome. The overarching aim of this work is to introduce SL to analysts and practitioners. This work compares the performance of logistic regression, penalized regression, random forests, deep learning neural networks, and SL to predict successful substance use disorders (SUD) treatment. A nationwide database including 99,013 SUD treatment patients was used. All algorithms were evaluated using the area under the receiver operating characteristic curve (AUC) in a test sample that was not included in the training sample used to fit the prediction models. AUC for the models ranged between 0.793 and 0.820. SL was superior to all but one of the algorithms compared. An explanation of SL steps is provided. SL is the first step in targeted learning, an analytic framework that yields double robust effect estimation and inference with fewer assumptions than the usual parametric methods. Different aspects of SL depending on the context, its function within the targeted learning framework, and the benefits of this methodology in the addiction field are discussed.
authors.affiliation: Instituto de Cálculo, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, CONICET, Buenos Aires, Argentina.|Iowa Consortium for Substance Abuse Research and Evaluation, University of Iowa, Iowa City, Iowa, United States of America.
authors.forename: Laura
authors.identifier: http://orcid.org/0000-0001-5213-6012
authors.initials: L
authors.lastname: Acion
authors.affiliation: Instituto de Cálculo, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, CONICET, Buenos Aires, Argentina.
authors.forename: Diana
authors.initials: D
authors.lastname: Kelmansky
authors.affiliation: Division of Biostatistics, University of California, Berkeley, California, United States of America.
authors.forename: Mark
authors.initials: M
authors.lastname: van der Laan
authors.affiliation: Iowa Consortium for Substance Abuse Research and Evaluation, University of Iowa, Iowa City, Iowa, United States of America.|Counseling Psychology Program, Department of Psychological and Quantitative Foundations, College of Education, University of Iowa, Iowa City, Iowa, United States of America.
authors.forename: Ethan
authors.initials: E
authors.lastname: Sahker
authors.affiliation: Iowa Consortium for Substance Abuse Research and Evaluation, University of Iowa, Iowa City, Iowa, United States of America.
authors.forename: DeShauna
authors.initials: D
authors.lastname: Jones
authors.affiliation: Iowa Consortium for Substance Abuse Research and Evaluation, University of Iowa, Iowa City, Iowa, United States of America.|Department of Psychiatry, Roy J and Lucille A Carver College of Medicine, University of Iowa, Iowa City, Iowa, United States of America.|Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, Iowa, United States of America.
authors.forename: Stephan
authors.initials: S
authors.lastname: Arndt
mesh_terms: D000293:Adolescent; D000328:Adult; D019540:Area Under Curve; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted; D005260:Female; D006801:Humans; D007902:Length of Stay; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011379:Prognosis; D012372:ROC Curve; D012044:Regression Analysis; D012959:Socioeconomic Factors; D019966:Substance-Related Disorders / Q000175:diagnosis* / Q000628:therapy*; D016896:Treatment Outcome; D055815:Young Adult