| Autor: |
Collin A; CentraleSupélec, Université Paris-Saclay, 91190 Gif-sur-Yvette, France., Ayuso-Muñoz A; Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, Spain., Tejera-Nevado P; Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, Spain., Prieto-Santamaría L; Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, Spain.; Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, 28660 Boadilla del Monte, Spain., Verdejo-García A; Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC 3800, Australia., Díaz-Batanero C; Clinical and Experimental Psychology Department, University of Huelva, 21071 Huelva, Spain.; Research Center for Natural Resources, Health and the Environment, University of Huelva, 21071 Huelva, Spain., Fernández-Calderón F; Clinical and Experimental Psychology Department, University of Huelva, 21071 Huelva, Spain.; Research Center for Natural Resources, Health and the Environment, University of Huelva, 21071 Huelva, Spain., Albein-Urios N; Discipline of Psychology, Federation University, Berwick, VIC 3806, Australia., Lozano ÓM; Clinical and Experimental Psychology Department, University of Huelva, 21071 Huelva, Spain.; Research Center for Natural Resources, Health and the Environment, University of Huelva, 21071 Huelva, Spain., Rodríguez-González A; Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, Spain.; Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, 28660 Boadilla del Monte, Spain. |
| Abstrakt: |
Background : Retention in treatment is crucial for the success of interventions targeting alcohol use disorder (AUD), which affects over 100 million people globally. Most previous studies have used classical statistical techniques to predict treatment dropout, and their results remain inconclusive. This study aimed to use novel machine learning tools to identify models that predict dropout with greater precision, enabling the development of better retention strategies for those at higher risk. Methods : A retrospective observational study of 39,030 (17.3% female) participants enrolled in outpatient-based treatment for alcohol use disorder in a state-wide public treatment network has been used. Participants were recruited between 1 January 2015 and 31 December 2019. We applied different machine learning algorithms to create models that allow one to predict the premature cessation of treatment (dropout). With the objective of increasing the explainability of those models with the best precision, considered as black-box models, explainability technique analyses were also applied. Results : Considering as the best models those obtained with one of the so-called black-box models (support vector classifier (SVC)), the results from the best model, from the explainability perspective, showed that the variables that showed greater explanatory capacity for treatment dropout are previous drug use as well as psychiatric comorbidity. Among these variables, those of having undergone previous opioid substitution treatment and receiving coordinated psychiatric care in mental health services showed the greatest capacity for predicting dropout. Conclusions : By using novel machine learning techniques on a large representative sample of patients enrolled in alcohol use disorder treatment, we have identified several machine learning models that help in predicting a higher risk of treatment dropout. Previous treatment for other substance use disorders (SUDs) and concurrent psychiatric comorbidity were the best predictors of dropout, and patients showing these characteristics may need more intensive or complementary interventions to benefit from treatment. |
