{
    "abstract": "Clinical staff are typically poor at predicting alcohol dependence treatment outcomes. Machine learning (ML) offers the potential to model complex clinical data more effectively. This study tested the predictive accuracy of ML algorithms demonstrated to be effective in predicting alcohol dependence outcomes, compared with clinical judgement and traditional linear regression.\nProspective study. ML models were trained on 1016 previously treated patients (training-set) who attended a hospital-based alcohol and drug clinic. ML models (n\u00a0=\u00a027), clinical psychologists (n\u00a0=\u00a010) and a 'traditional' logistic regression model (n\u00a0=\u00a01) predicted treatment outcome during the initial treatment session of an alcohol dependence programme.\nA 12-week cognitive behavioural therapy (CBT)-based abstinence programme for alcohol dependence in a hospital-based alcohol and drug clinic in Australia.\nProspective predictions were made for 220 new patients (test-set; 70.91% male, mean age\u00a0=\u00a035.78\u00a0years, standard deviation\u00a0=\u00a09.19). Sixty-nine (31.36%) patients successfully completed treatment.\nTreatment success was the primary outcome variable. The cross-validated training-set accuracy of ML models was used to determine optimal parameters for selecting models for prospective prediction. Accuracy, sensitivity, specificity, area under the receiver operator curve (AUC), Brier score and calibration curves were calculated and compared across predictions.\nThe mean aggregate accuracy of the ML models (63.06%) was higher than the mean accuracy of psychologist predictions (56.36%). The most accurate ML model achieved 70% accuracy, as did logistic regression. Both were more accurate than psychologists (P\u00a0<\u00a00.05) and had superior calibration. The high specificity for the selected ML (79%) and logistic regression (90%) meant they were significantly (P\u00a0<\u00a00.001) more effective than psychologists (50%) at correctly identifying patients whose treatment was unsuccessful. For ML and logistic regression, high specificity came at the expense of sensitivity (26 and 31%, respectively), resulting in poor prediction of successful patients.\nMachine learning models and logistic regression appear to be more accurate than psychologists at predicting treatment outcomes in an abstinence programme for alcohol dependence, but sensitivity is low.",
    "authors": [
        {
            "affiliation": "Alcohol and Drug Assessment Unit, Princess Alexandra Hospital, Brisbane, Australia.\nDiscipline of Psychiatry, The University of Queensland, Brisbane, Australia.\nNational Health and Medical Research Council FASD Research Australia Centre of Research Excellence, Telethon Kids Institute, The University of Western Australia, Perth, Australia.",
            "firstname": "Martyn",
            "initials": "M",
            "lastname": "Symons"
        },
        {
            "affiliation": "Alcohol and Drug Assessment Unit, Princess Alexandra Hospital, Brisbane, Australia.\nCentre for Youth Substance Abuse Research, The University of Queensland, Brisbane, Australia.",
            "firstname": "Gerald F X",
            "initials": "GFX",
            "lastname": "Feeney"
        },
        {
            "affiliation": "School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia.",
            "firstname": "Marcus R",
            "initials": "MR",
            "lastname": "Gallagher"
        },
        {
            "affiliation": "Alcohol and Drug Assessment Unit, Princess Alexandra Hospital, Brisbane, Australia.\nFaculty of Health, Queensland University of Technology, Brisbane, Australia.",
            "firstname": "Ross McD",
            "initials": "RM",
            "lastname": "Young"
        },
        {
            "affiliation": "Alcohol and Drug Assessment Unit, Princess Alexandra Hospital, Brisbane, Australia.\nDiscipline of Psychiatry, The University of Queensland, Brisbane, Australia.\nCentre for Youth Substance Abuse Research, The University of Queensland, Brisbane, Australia.",
            "firstname": "Jason P",
            "initials": "JP",
            "lastname": "Connor"
        }
    ],
    "conclusions": null,
    "copyrights": "\u00a9 2020 Society for the Study of Addiction.",
    "doi": "10.1111/add.15038",
    "journal": "Addiction (Abingdon, England)",
    "keywords": [
        "Addiction",
        "alcohol use disorder",
        "cognitive behavioural therapy",
        "machine learning",
        "prediction",
        "psychologists"
    ],
    "methods": null,
    "publication_date": "2020-03-10",
    "pubmed_id": "32150316",
    "results": null,
    "title": "Predicting alcohol dependence treatment outcomes: a prospective comparative study of clinical psychologists versus 'trained' machine learning models.",
    "xml": "<Element 'PubmedArticle' at 0x7885032136f0>"
}