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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. © 2017 Acion et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Registro:

Documento: Artículo
Título:Use of a machine learning framework to predict substance use disorder treatment success
Autor:Acion, L.; Kelmansky, D.; Laan, M.D.V.; Sahker, E.; Jones, D.; Arndt, S.
Filiación: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, IA, United States
Division of Biostatistics, University of California, Berkeley, CA, United States
Counseling Psychology Program, Department of Psychological and Quantitative Foundations, College of Education, University of Iowa, Iowa City, IA, United States
Department of Psychiatry, Roy J and Lucille A Carver College of Medicine, University of Iowa, Iowa City, IA, United States
Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, IA, United States
Palabras clave:adult; algorithm; area under the curve; Article; artificial neural network; controlled study; decision making; drug dependence; education; employment status; female; Hispanic; human; length of stay; machine learning; major clinical study; male; methodology; prediction; receiver operating characteristic; sensitivity analysis; substance abuse; super learning; treatment outcome; adolescent; computer assisted diagnosis; drug dependence; factual database; middle aged; prognosis; regression analysis; socioeconomics; young adult; Adolescent; Adult; Area Under Curve; Databases, Factual; Diagnosis, Computer-Assisted; Female; Humans; Length of Stay; Machine Learning; Male; Middle Aged; Neural Networks (Computer); Prognosis; Regression Analysis; ROC Curve; Socioeconomic Factors; Substance-Related Disorders; Treatment Outcome; Young Adult
Año:2017
Volumen:12
Número:4
DOI: http://dx.doi.org/10.1371/journal.pone.0175383
Título revista:PLoS ONE
Título revista abreviado:PLoS ONE
ISSN:19326203
CODEN:POLNC
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_19326203_v12_n4_p_Acion

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Citas:

---------- APA ----------
Acion, L., Kelmansky, D., Laan, M.D.V., Sahker, E., Jones, D. & Arndt, S. (2017) . Use of a machine learning framework to predict substance use disorder treatment success. PLoS ONE, 12(4).
http://dx.doi.org/10.1371/journal.pone.0175383
---------- CHICAGO ----------
Acion, L., Kelmansky, D., Laan, M.D.V., Sahker, E., Jones, D., Arndt, S. "Use of a machine learning framework to predict substance use disorder treatment success" . PLoS ONE 12, no. 4 (2017).
http://dx.doi.org/10.1371/journal.pone.0175383
---------- MLA ----------
Acion, L., Kelmansky, D., Laan, M.D.V., Sahker, E., Jones, D., Arndt, S. "Use of a machine learning framework to predict substance use disorder treatment success" . PLoS ONE, vol. 12, no. 4, 2017.
http://dx.doi.org/10.1371/journal.pone.0175383
---------- VANCOUVER ----------
Acion, L., Kelmansky, D., Laan, M.D.V., Sahker, E., Jones, D., Arndt, S. Use of a machine learning framework to predict substance use disorder treatment success. PLoS ONE. 2017;12(4).
http://dx.doi.org/10.1371/journal.pone.0175383