Martyn Symons1, Gerald F X Feeney2, Marcus R Gallagher3, Ross McD Young4, Jason P Connor5. 1. Alcohol and Drug Assessment Unit, Princess Alexandra Hospital, Wooloongabba, Brisbane, Queensland 4102, Australia; Discipline of Psychiatry, The University of Queensland, K Floor, Mental Health Centre, Royal Brisbane and Women's Hospital, Herston, Brisbane, Queensland 4029, Australia; Telethon Kids Institute, West Perth, Western Australia 6872, Australia. 2. Alcohol and Drug Assessment Unit, Princess Alexandra Hospital, Wooloongabba, Brisbane, Queensland 4102, Australia; Centre for Youth Substance Abuse Research, The University of Queensland, Upland Road, St Lucia, Brisbane, Queensland 4072, Australia. 3. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland 4072, Australia. 4. Alcohol and Drug Assessment Unit, Princess Alexandra Hospital, Wooloongabba, Brisbane, Queensland 4102, Australia; Faculty of Health, Queensland University of Technology, Kelvin Grove, Brisbane, Queensland 4059, Australia. 5. Alcohol and Drug Assessment Unit, Princess Alexandra Hospital, Wooloongabba, Brisbane, Queensland 4102, Australia; Discipline of Psychiatry, The University of Queensland, K Floor, Mental Health Centre, Royal Brisbane and Women's Hospital, Herston, Brisbane, Queensland 4029, Australia; Centre for Youth Substance Abuse Research, The University of Queensland, Upland Road, St Lucia, Brisbane, Queensland 4072, Australia. Electronic address: Jason.Connor@uq.edu.au.
Abstract
BACKGROUND AND OBJECTIVES: 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. METHODS: 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 model were examined. RESULTS: The most accurate ML model (Fuzzy Unordered Rule Induction Algorithm, 74%) was significantly more accurate than the four least accurate clinical staff (51%-40%). However, the robustness of this finding may be limited by the moderate area under the receiver operator curve (AUC = 0.49). There was no significant difference in mean aggregate predictive accuracy between 10 clinical staff (56.1%) and the 28 best models (58.57%). Addiction therapists favoured demographic and consumption variables compared with the ML model using more questionnaire subscales. CONCLUSIONS: The majority of staff and ML models were not more accurate than suggested by chance. However, the best performing prediction models may provide useful adjunctive information to standard clinically available prognostic data to more effectively target treatment approaches in clinical settings.
BACKGROUND AND OBJECTIVES: 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. METHODS: 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 model were examined. RESULTS: The most accurate ML model (Fuzzy Unordered Rule Induction Algorithm, 74%) was significantly more accurate than the four least accurate clinical staff (51%-40%). However, the robustness of this finding may be limited by the moderate area under the receiver operator curve (AUC = 0.49). There was no significant difference in mean aggregate predictive accuracy between 10 clinical staff (56.1%) and the 28 best models (58.57%). Addiction therapists favoured demographic and consumption variables compared with the ML model using more questionnaire subscales. CONCLUSIONS: The majority of staff and ML models were not more accurate than suggested by chance. However, the best performing prediction models may provide useful adjunctive information to standard clinically available prognostic data to more effectively target treatment approaches in clinical settings.
Authors: Lucas A Ramos; Matthijs Blankers; Guido van Wingen; Tamara de Bruijn; Steffen C Pauws; Anneke E Goudriaan Journal: Front Psychol Date: 2021-09-03