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Use of a machine learning framework to predict substance use disorder treatment success

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  • Laura Acion
  • Diana Kelmansky
  • Mark van der Laan
  • Ethan Sahker
  • DeShauna Jones
  • Stephan Arndt

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.

Suggested Citation

  • Laura Acion & Diana Kelmansky & Mark van der Laan & Ethan Sahker & DeShauna Jones & Stephan Arndt, 2017. "Use of a machine learning framework to predict substance use disorder treatment success," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-14, April.
  • Handle: RePEc:plo:pone00:0175383
    DOI: 10.1371/journal.pone.0175383
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    References listed on IDEAS

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    1. Evans, Elizabeth & Li, Libo & Hser, Yih-Ing, 2009. "Client and program factors associated with dropout from court mandated drug treatment," Evaluation and Program Planning, Elsevier, vol. 32(3), pages 204-212, August.
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    1. Franck Jaotombo & Vanessa Pauly & Guillaume Fond & Veronica Orleans & Pascal Auquier & Badih Ghattas & Laurent Boyer, 2023. "Machine-learning prediction for hospital length of stay using a French medico-administrative database," Post-Print hal-04325691, HAL.
    2. Alexander Engels & Katrin C Reber & Ivonne Lindlbauer & Kilian Rapp & Gisela Büchele & Jochen Klenk & Andreas Meid & Clemens Becker & Hans-Helmut König, 2020. "Osteoporotic hip fracture prediction from risk factors available in administrative claims data – A machine learning approach," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-14, May.
    3. Sheelu Sagar & Rohit Rastogi & Vikas Garg & Ishwar V. Basavaraddi, 2022. "Impact of Meditation on Quality of Life of Employees," International Journal of Reliable and Quality E-Healthcare (IJRQEH), IGI Global, vol. 11(1), pages 1-16, January.
    4. Vinícius Serafini Roglio & Eduardo Nunes Borges & Francisco Diego Rabelo-da-Ponte & Felipe Ornell & Juliana Nichterwitz Scherer & Jaqueline Bohrer Schuch & Ives Cavalcante Passos & Breno Sanvicente-Vi, 2020. "Prediction of attempted suicide in men and women with crack-cocaine use disorder in Brazil," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-19, May.

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