Machine learning vs addiction therapists: A pilot study predicting alcohol dependence treatment outcome from patient data in behavior therapy with adjunctive medication

Journal of Substance Abuse Treatment
M SymonsJason P Connor

Abstract

Clinical staff providing addiction treatment predict patient outcome poorly. Prognoses based on linear statistics are rarely replicated. Addiction is a complex non-linear behavior. Incorporating non-linear models, Machine Learning (ML) has successfully predicted treatment outcome when applied in other areas of medicine. Using identical assessment data across the two groups, this study compares the accuracy of ML models versus clinical staff to predict alcohol dependence treatment outcome in behavior therapy using patient data only. Machine learning models (n = 28) were constructed ('trained') using demographic and psychometric assessment data from 780 previously treated patients who had undertaken a 12 week, abstinence-based Cognitive Behavioral Therapy program for alcohol dependence. Independent predictions applying assessment data for an additional 50 consecutive patients were obtained from 10 experienced addiction therapists and the 28 trained ML models. The predictive accuracy of the ML models and the addiction therapists was then compared with further investigation of the 10 best models selected by cross-validated accuracy on the training-set. Variables selected as important for prediction by staff and the most accurate ML...Continue Reading

Citations

Apr 19, 2020·Current Opinion in Psychiatry·Elan BarenholtzWilliam Edward Hahn
Aug 31, 2020·Psychotherapy Research : Journal of the Society for Psychotherapy Research·Katie Aafjes-van DoornMarc Aafjes
Dec 12, 2020·Fortschritte der Neurologie-Psychiatrie·David PopovicNikolaos Koutsouleris

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