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Hilbert K, Böhnlein J, Meinke C, Chavanne AV, Langhammer T, Stumpe L, Winter N, Leenings R, Adolph D, Arolt V, Bischoff S, Cwik JC, Deckert J, Domschke K, Fydrich T, Gathmann B, Hamm AO, Heinig I, Herrmann MJ, Hollandt M, Hoyer J, Junghöfer M, Kircher T, Koelkebeck K, Lotze M, Margraf J, Mumm JLM, Neudeck P, Pauli P, Pittig A, Plag J, Richter J, Ridderbusch IC, Rief W, Schneider S, Schwarzmeier H, Seeger FR, Siminski N, Straube B, Straube T, Ströhle A, Wittchen HU, Wroblewski A, Yang Y, Roesmann K, Leehr EJ, Dannlowski U, Lueken U. Lack of evidence for predictive utility from resting state fMRI data for individual exposure-based cognitive behavioral therapy outcomes: A machine learning study in two large multi-site samples in anxiety disorders. Neuroimage 2024; 295:120639. [PMID: 38796977 DOI: 10.1016/j.neuroimage.2024.120639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 05/03/2024] [Accepted: 05/06/2024] [Indexed: 05/29/2024] Open
Abstract
Data-based predictions of individual Cognitive Behavioral Therapy (CBT) treatment response are a fundamental step towards precision medicine. Past studies demonstrated only moderate prediction accuracy (i.e. ability to discriminate between responders and non-responders of a given treatment) when using clinical routine data such as demographic and questionnaire data, while neuroimaging data achieved superior prediction accuracy. However, these studies may be considerably biased due to very limited sample sizes and bias-prone methodology. Adequately powered and cross-validated samples are a prerequisite to evaluate predictive performance and to identify the most promising predictors. We therefore analyzed resting state functional magnet resonance imaging (rs-fMRI) data from two large clinical trials to test whether functional neuroimaging data continues to provide good prediction accuracy in much larger samples. Data came from two distinct German multicenter studies on exposure-based CBT for anxiety disorders, the Protect-AD and SpiderVR studies. We separately and independently preprocessed baseline rs-fMRI data from n = 220 patients (Protect-AD) and n = 190 patients (SpiderVR) and extracted a variety of features, including ROI-to-ROI and edge-functional connectivity, sliding-windows, and graph measures. Including these features in sophisticated machine learning pipelines, we found that predictions of individual outcomes never significantly differed from chance level, even when conducting a range of exploratory post-hoc analyses. Moreover, resting state data never provided prediction accuracy beyond the sociodemographic and clinical data. The analyses were independent of each other in terms of selecting methods to process resting state data for prediction input as well as in the used parameters of the machine learning pipelines, corroborating the external validity of the results. These similar findings in two independent studies, analyzed separately, urge caution regarding the interpretation of promising prediction results based on neuroimaging data from small samples and emphasizes that some of the prediction accuracies from previous studies may result from overestimation due to homogeneous data and weak cross-validation schemes. The promise of resting-state neuroimaging data to play an important role in the prediction of CBT treatment outcomes in patients with anxiety disorders remains yet to be delivered.
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Affiliation(s)
- Kevin Hilbert
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany; Department of Psychology, HMU Health and Medical University Erfurt, Erfurt, Germany
| | - Joscha Böhnlein
- Institute for Translational Psychiatry, University of Münster, Germany.
| | - Charlotte Meinke
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Alice V Chavanne
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany; Université Paris-Saclay, INSERM U1299 "Trajectoires développementales et psychiatrie", CNRS UMR 9010 Centre Borelli, Ecole Normale Supérieure Paris-Saclay, France
| | - Till Langhammer
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Lara Stumpe
- Institute for Translational Psychiatry, University of Münster, Germany
| | - Nils Winter
- Institute for Translational Psychiatry, University of Münster, Germany
| | - Ramona Leenings
- Institute for Translational Psychiatry, University of Münster, Germany
| | - Dirk Adolph
- Mental Health Research and Treatment Center, Faculty of Psychology, Ruhr-Universität Bochum, Bochum, Germany
| | - Volker Arolt
- Institute for Translational Psychiatry, University of Münster, Germany
| | - Sophie Bischoff
- Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Jan C Cwik
- Department of Clinical Psychology and Psychotherapy, Faculty of Human Sciences, Universität zu Köln, Germany
| | - Jürgen Deckert
- Center for Mental Health, Department of Psychiatry, Psychosomatics, and Psychotherapy, University Hospital of Würzburg, Germany
| | - Katharina Domschke
- Department of Psychiatry and Psychotherapy, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Thomas Fydrich
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Bettina Gathmann
- Institute of Medical Psychology and Systems Neuroscience, University of Münster, Germany
| | - Alfons O Hamm
- Department of Biological and Clinical Psychology, University of Greifswald, Greifswald, Germany
| | - Ingmar Heinig
- Institute of Clinical Psychology & Psychotherapy, Technische Universität Dresden, Dresden, Germany
| | - Martin J Herrmann
- Center for Mental Health, Department of Psychiatry, Psychosomatics, and Psychotherapy, University Hospital of Würzburg, Germany
| | - Maike Hollandt
- Department of Biological and Clinical Psychology, University of Greifswald, Greifswald, Germany
| | - Jürgen Hoyer
- Institute of Clinical Psychology & Psychotherapy, Technische Universität Dresden, Dresden, Germany
| | - Markus Junghöfer
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Katja Koelkebeck
- LVR-University-Hospital Essen, Department of Psychiatry and Psychotherapy, University of Duisburg-Essen, Essen, Germany
| | - Martin Lotze
- Functional Imaging Unit. Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Jürgen Margraf
- Mental Health Research and Treatment Center, Faculty of Psychology, Ruhr-Universität Bochum, Bochum, Germany
| | - Jennifer L M Mumm
- Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Peter Neudeck
- Protect-AD Study Site Cologne, Cologne, Germany; Institut für Klinische Psychologie und Psychotherapie, TU Chemnitz, Germany
| | - Paul Pauli
- Department of Psychology, University of Würzburg, Würzburg, Germany
| | - Andre Pittig
- Translational Psychotherapy, Institute of Psychology, University of Göttingen, Germany
| | - Jens Plag
- Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité - Universitätsmedizin Berlin, Berlin, Germany; Department of Psychiatry, Psychotherapy and Psychosomatics, Alexianer Krankenhaus Hedwigshoehe, St. Hedwig Kliniken, Berlin, Germany
| | - Jan Richter
- Department of Biological and Clinical Psychology, University of Greifswald, Greifswald, Germany; Department of Experimental Psychopathology, University of Hildesheim, Hildesheim, Germany
| | | | - Winfried Rief
- Department of Clinical Psychology and Psychotherapy, Faculty of Psychology & Center for Mind, Brain and Behavior - CMBB, Philipps-University of Marburg, Marburg, Germany
| | - Silvia Schneider
- Faculty of Psychology, Clinical Child and Adolescent Psychology, Mental Health Research and Treatment Center, Ruhr-Universität Bochum, Bochum, Germany
| | - Hanna Schwarzmeier
- Center for Mental Health, Department of Psychiatry, Psychosomatics, and Psychotherapy, University Hospital of Würzburg, Germany
| | - Fabian R Seeger
- Center for Mental Health, Department of Psychiatry, Psychosomatics, and Psychotherapy, University Hospital of Würzburg, Germany
| | - Niklas Siminski
- Center for Mental Health, Department of Psychiatry, Psychosomatics, and Psychotherapy, University Hospital of Würzburg, Germany
| | - Benjamin Straube
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Thomas Straube
- Institute of Psychology, Unit of Clinical Psychology and Psychotherapy in Childhood and Adolescence, University of Osnabrueck, Osnabruck, Germany
| | - Andreas Ströhle
- Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | | | - Adrian Wroblewski
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Yunbo Yang
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Kati Roesmann
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Germany; Institute of Psychology, Unit of Clinical Psychology and Psychotherapy in Childhood and Adolescence, University of Osnabrueck, Osnabruck, Germany
| | - Elisabeth J Leehr
- Institute for Translational Psychiatry, University of Münster, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Germany
| | - Ulrike Lueken
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany; German Center for Mental Health (DZPG), partner site Berlin/Potsdam, Germany
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Hornstein S, Scharfenberger J, Lueken U, Wundrack R, Hilbert K. Predicting recurrent chat contact in a psychological intervention for the youth using natural language processing. NPJ Digit Med 2024; 7:132. [PMID: 38762694 PMCID: PMC11102489 DOI: 10.1038/s41746-024-01121-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 04/23/2024] [Indexed: 05/20/2024] Open
Abstract
Chat-based counseling hotlines emerged as a promising low-threshold intervention for youth mental health. However, despite the resulting availability of large text corpora, little work has investigated Natural Language Processing (NLP) applications within this setting. Therefore, this preregistered approach (OSF: XA4PN) utilizes a sample of approximately 19,000 children and young adults that received a chat consultation from a 24/7 crisis service in Germany. Around 800,000 messages were used to predict whether chatters would contact the service again, as this would allow the provision of or redirection to additional treatment. We trained an XGBoost Classifier on the words of the anonymized conversations, using repeated cross-validation and bayesian optimization for hyperparameter search. The best model was able to achieve an AUROC score of 0.68 (p < 0.01) on the previously unseen 3942 newest consultations. A shapely-based explainability approach revealed that words indicating younger age or female gender and terms related to self-harm and suicidal thoughts were associated with a higher chance of recontacting. We conclude that NLP-based predictions of recurrent contact are a promising path toward personalized care at chat hotlines.
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Affiliation(s)
- Silvan Hornstein
- Department of Psychology, Humboldt-Universität zu Berlin, 10099 Berlin, Germany.
| | | | - Ulrike Lueken
- Department of Psychology, Humboldt-Universität zu Berlin, 10099 Berlin, Germany
- German Center for Mental Health (DZPG), partner site Berlin/Potsdam, Potsdam, Germany
| | - Richard Wundrack
- Department of Psychology, Humboldt-Universität zu Berlin, 10099 Berlin, Germany
| | - Kevin Hilbert
- Department of Psychology, Humboldt-Universität zu Berlin, 10099 Berlin, Germany
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Zantvoort K, Hentati Isacsson N, Funk B, Kaldo V. Dataset size versus homogeneity: A machine learning study on pooling intervention data in e-mental health dropout predictions. Digit Health 2024; 10:20552076241248920. [PMID: 38757087 PMCID: PMC11097733 DOI: 10.1177/20552076241248920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 04/04/2024] [Indexed: 05/18/2024] Open
Abstract
Objective This study proposes a way of increasing dataset sizes for machine learning tasks in Internet-based Cognitive Behavioral Therapy through pooling interventions. To this end, it (1) examines similarities in user behavior and symptom data among online interventions for patients with depression, social anxiety, and panic disorder and (2) explores whether these similarities suffice to allow for pooling the data together, resulting in more training data when prediction intervention dropout. Methods A total of 6418 routine care patients from the Internet Psychiatry in Stockholm are analyzed using (1) clustering and (2) dropout prediction models. For the latter, prediction models trained on each individual intervention's data are compared to those trained on all three interventions pooled into one dataset. To investigate if results vary with dataset size, the prediction is repeated using small and medium dataset sizes. Results The clustering analysis identified three distinct groups that are almost equally spread across interventions and are instead characterized by different activity levels. In eight out of nine settings investigated, pooling the data improves prediction results compared to models trained on a single intervention dataset. It is further confirmed that models trained on small datasets are more likely to overestimate prediction results. Conclusion The study reveals similar patterns of patients with depression, social anxiety, and panic disorder regarding online activity and intervention dropout. As such, this work offers pooling different interventions' data as a possible approach to counter the problem of small dataset sizes in psychological research.
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Affiliation(s)
- Kirsten Zantvoort
- Institute of Information Systems, Leuphana University, Lueneburg, Germany
| | - Nils Hentati Isacsson
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, & Stockholm Health Care Services, Stockholm, Sweden
| | - Burkhardt Funk
- Institute of Information Systems, Leuphana University, Lueneburg, Germany
| | - Viktor Kaldo
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, & Stockholm Health Care Services, Stockholm, Sweden
- Department of Psychology, Faculty of Health and Life Sciences, Linnaeus University, Växjö, Sweden
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Tomko RL, Wolf BJ, McClure EA, Carpenter MJ, Magruder KM, Squeglia LM, Gray KM. Who responds to a multi-component treatment for cannabis use disorder? Using multivariable and machine learning models to classify treatment responders and non-responders. Addiction 2023; 118:1965-1974. [PMID: 37132085 PMCID: PMC10524796 DOI: 10.1111/add.16226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 04/13/2023] [Indexed: 05/04/2023]
Abstract
BACKGROUND AND AIMS Treatments for cannabis use disorder (CUD) have limited efficacy and little is known about who responds to existing treatments. Accurately predicting who will respond to treatment can improve clinical decision-making by allowing clinicians to offer the most appropriate level and type of care. This study aimed to determine whether multivariable/machine learning models can be used to classify CUD treatment responders versus non-responders. METHODS This secondary analysis used data from a National Drug Abuse Treatment Clinical Trials Network multi-site outpatient clinical trial in the United States. Adults with CUD (n = 302) received 12 weeks of contingency management, brief cessation counseling and were randomized to receive additionally either (1) N-Acetylcysteine or (2) placebo. Multivariable/machine learning models were used to classify treatment responders (i.e. two consecutive negative urine cannabinoid tests or a 50% reduction in days of use) versus non-responders using baseline demographic, medical, psychiatric and substance use information. RESULTS Prediction performance for various machine learning and regression prediction models yielded area under the curves (AUCs) >0.70 for four models (0.72-0.77), with support vector machine models having the highest overall accuracy (73%; 95% CI = 68-78%) and AUC (0.77; 95% CI = 0.72, 0.83). Fourteen variables were retained in at least three of four top models, including demographic (ethnicity, education), medical (diastolic/systolic blood pressure, overall health, neurological diagnosis), psychiatric (depressive symptoms, generalized anxiety disorder, antisocial personality disorder) and substance use (tobacco smoker, baseline cannabinoid level, amphetamine use, age of experimentation with other substances, cannabis withdrawal intensity) characteristics. CONCLUSIONS Multivariable/machine learning models can improve on chance prediction of treatment response to outpatient cannabis use disorder treatment, although further improvements in prediction performance are likely necessary for decisions about clinical care.
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Affiliation(s)
- Rachel L. Tomko
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Bethany J. Wolf
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Erin A. McClure
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Matthew J. Carpenter
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA
- Hollings Cancer Center, Medical University of South Carolina, Charleston, SC, USA
| | - Kathryn M. Magruder
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Lindsay M. Squeglia
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Kevin M. Gray
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA
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Yao L, Wang Z, Gu H, Zhao X, Chen Y, Liu L. Prediction of Chinese clients' satisfaction with psychotherapy by machine learning. Front Psychiatry 2023; 14:947081. [PMID: 36741124 PMCID: PMC9893506 DOI: 10.3389/fpsyt.2023.947081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 01/02/2023] [Indexed: 01/20/2023] Open
Abstract
Background Effective psychotherapy should satisfy the client, but that satisfaction depends on many factors. We do not fully understand the factors that affect client satisfaction with psychotherapy and how these factors synergistically affect a client's psychotherapy experience. Aims This study aims to use machine learning to predict Chinese clients' satisfaction with psychotherapy and analyze potential outcome contributors. Methods In this cross-sectional investigation, a self-compiled online questionnaire was delivered through the WeChat app. The information of 791 participants who had received psychotherapy was used in the study. A series of features, for example, the participants' demographic features and psychotherapy-related features, were chosen to distinguish between participants satisfied and dissatisfied with the psychotherapy they received. With our dataset, we trained seven supervised machine-learning-based algorithms to implement prediction models. Results Among the 791 participants, 619 (78.3%) reported being satisfied with the psychotherapy sessions that they received. The occupation of the clients, the location of psychotherapy, and the form of access to psychotherapy are the three most recognizable features that determined whether clients are satisfied with psychotherapy. The machine-learning model based on the CatBoost achieved the highest prediction performance in classifying satisfied and psychotherapy clients with an F1 score of 0.758. Conclusion This study clarified the factors related to clients' satisfaction with psychotherapy, and the machine-learning-based classifier accurately distinguished clients who were satisfied or unsatisfied with psychotherapy. These results will help provide better psychotherapy strategies for specific clients, so they may achieve better therapeutic outcomes.
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Affiliation(s)
- Lijun Yao
- Clinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China
| | - Ziyi Wang
- Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China
| | - Hong Gu
- Clinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China
| | - Xudong Zhao
- Clinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China
| | - Yang Chen
- Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China
| | - Liang Liu
- Clinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China
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Can we predict who will benefit from cognitive-behavioural therapy? A systematic review and meta-analysis of machine learning studies. Clin Psychol Rev 2022; 97:102193. [DOI: 10.1016/j.cpr.2022.102193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 06/29/2022] [Accepted: 08/04/2022] [Indexed: 11/23/2022]
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Marti-Puig P, Capra C, Vega D, Llunas L, Solé-Casals J. A Machine Learning Approach for Predicting Non-Suicidal Self-Injury in Young Adults. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22134790. [PMID: 35808286 PMCID: PMC9269418 DOI: 10.3390/s22134790] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 06/20/2022] [Accepted: 06/22/2022] [Indexed: 05/11/2023]
Abstract
Artificial intelligence techniques were explored to assess the ability to anticipate self-harming behaviour in the mental health context using a database collected by an app previously designed to record the emotional states and activities of a group of subjects exhibiting self-harm. Specifically, the Leave-One-Subject-Out technique was used to train classification trees with a maximum of five splits. The results show an accuracy of 84.78%, a sensitivity of 64.64% and a specificity of 85.53%. In addition, positive and negative predictive values were also obtained, with results of 14.48% and 98.47%, respectively. These results are in line with those reported in previous work using a multilevel mixed-effect regression analysis. The combination of apps and AI techniques is a powerful way to improve the tools to accompany and support the care and treatment of patients with this type of behaviour. These studies also guide the improvement of apps on the user side, simplifying and collecting more meaningful data, and on the therapist side, progressing in pathology treatments. Traditional therapy involves observing and reconstructing what had happened before episodes once they have occurred. This new generation of tools will make it possible to monitor the pathology more closely and to act preventively.
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Affiliation(s)
- Pere Marti-Puig
- Data and Signal Processing Group, University of Vic—Central University of Catalonia, 08500 Vic, Catalonia, Spain; (P.M.-P.); (C.C.)
| | - Chiara Capra
- Data and Signal Processing Group, University of Vic—Central University of Catalonia, 08500 Vic, Catalonia, Spain; (P.M.-P.); (C.C.)
- beHIT, Carrer de Mata 1, 08004 Barcelona, Spain;
| | - Daniel Vega
- Psychiatry and Mental Health Department, Hospital Universitari d’Igualada, Consorci Sanitari de l’Anoia & Fundació Sanitària d’Igualada, 08700 Igualada, Barcelona, Spain;
- Department of Psychiatry and Forensic Medicine, Institute of Neurosciences, Universitat Autònoma de Barcelona (UAB), 08193 Cerdanyola del Vallés, Barcelona, Spain
| | - Laia Llunas
- beHIT, Carrer de Mata 1, 08004 Barcelona, Spain;
| | - Jordi Solé-Casals
- Data and Signal Processing Group, University of Vic—Central University of Catalonia, 08500 Vic, Catalonia, Spain; (P.M.-P.); (C.C.)
- Correspondence: ; Tel.: +34-93-8815519
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Zhou Y, Chen XY, Liu D, Pan YL, Hou YF, Gao TT, Peng F, Wang XC, Zhang XY. Predicting first session working alliances using deep learning algorithms: A proof-of-concept study for personalized psychotherapy. Psychother Res 2022; 32:1100-1109. [PMID: 35635836 DOI: 10.1080/10503307.2022.2078680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
Affiliation(s)
- Ying Zhou
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, People’s Republic of China
| | - Xiao-yu Chen
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, People’s Republic of China
| | - Ding Liu
- College of Psychology, Shenzhen University, Shenzhen, People’s Republic of China
| | - Yu-lin Pan
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, People’s Republic of China
| | - Yan-fei Hou
- Department of Humanities and Mental Nursing, School of Nursing, Southern Medical University, Guangzhou, People’s Republic of China
| | - Ting-ting Gao
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, People’s Republic of China
| | - Fei Peng
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, People’s Republic of China
- Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, People’s Republic of China
| | - Xiao-cong Wang
- Department of Obstetrics and Gynaecology, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China
| | - Xiao-yuan Zhang
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, People’s Republic of China
- Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, People’s Republic of China
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Roberts W, Zhao Y, Verplaetse T, Moore KE, Peltier MR, Burke C, Zakiniaeiz Y, McKee S. Using machine learning to predict heavy drinking during outpatient alcohol treatment. Alcohol Clin Exp Res 2022; 46:657-666. [PMID: 35420710 PMCID: PMC9180421 DOI: 10.1111/acer.14802] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 02/15/2022] [Accepted: 02/22/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Accurate clinical prediction supports the effective treatment of alcohol use disorder (AUD) and other psychiatric disorders. Traditional statistical techniques have identified patient characteristics associated with treatment outcomes. However, less work has focused on systematically leveraging these associations to create optimal predictive models. The current study demonstrates how machine learning can be used to predict clinical outcomes in people completing outpatient AUD treatment. METHOD We used data from the COMBINE multisite clinical trial (n = 1383) to develop and test predictive models. We identified three priority prediction targets, including (1) heavy drinking during the first month of treatment, (2) heavy drinking during the last month of treatment, and (3) heavy drinking between weekly/bi-weekly sessions. Models were generated using the random forest algorithm. We used "leave sites out" partitioning to externally validate the models in trial sites that were not included in the model training. Stratified model development was used to test for sex differences in the relative importance of predictive features. RESULTS Models predicting heavy alcohol use during the first and last months of treatment showed internal cross-validation area under the curve (AUC) scores ranging from 0.67 to 0.74. AUC was comparable in the external validation using data from held-out sites (AUC range = 0.69 to 0.72). The model predicting between-session heavy drinking showed strong classification accuracy in internal cross-validation (AUC = 0.89) and external test samples (AUC range = 0.80 to 0.87). Stratified analyses showed substantial sex differences in optimal feature sets. CONCLUSION Machine learning techniques can predict alcohol treatment outcomes using routinely collected clinical data. This technique has the potential to greatly improve clinical prediction accuracy without requiring expensive or invasive assessment methods. More research is needed to understand how best to deploy these models.
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Affiliation(s)
- Walter Roberts
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA.,Department of Psychology, East Tennessee State University, Johnson City, Tennessee, USA
| | - Yize Zhao
- Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
| | - Terril Verplaetse
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Kelly E Moore
- Department of Psychology, East Tennessee State University, Johnson City, Tennessee, USA
| | - MacKenzie R Peltier
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA.,Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut, USA
| | - Catherine Burke
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Yasmin Zakiniaeiz
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Sherry McKee
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
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Hornstein S, Forman-Hoffman V, Nazander A, Ranta K, Hilbert K. Predicting therapy outcome in a digital mental health intervention for depression and anxiety: A machine learning approach. Digit Health 2021; 7:20552076211060659. [PMID: 34868624 PMCID: PMC8637697 DOI: 10.1177/20552076211060659] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 10/30/2021] [Indexed: 01/19/2023] Open
Abstract
Objective Predicting the outcomes of individual participants for treatment interventions appears central to making mental healthcare more tailored and effective. However, little work has been done to investigate the performance of machine learning-based predictions within digital mental health interventions. Therefore, this study evaluates the performance of machine learning in predicting treatment response in a digital mental health intervention designed for treating depression and anxiety. Methods Several algorithms were trained based on the data of 970 participants to predict a significant reduction in depression and anxiety symptoms using clinical and sociodemographic variables. As a random forest classifier performed best over cross-validation, it was used to predict the outcomes of 279 new participants. Results The random forest achieved an accuracy of 0.71 for the test set (base rate: 0.67, area under curve (AUC): 0.60, p = 0.001, balanced accuracy: 0.60). Additionally, predicted non-responders showed less average reduction of their Patient Health Questionnaire-9 (PHQ-9) (-2.7, p = 0.004) and General Anxiety Disorder Screener-7 values (-3.7, p < 0.001) compared to responders. Besides pre-treatment Patient Health Questionnaire-9 and General Anxiety Disorder Screener-7 values, the self-reported motivation, type of referral into the programme (self vs. healthcare provider) as well as Work Productivity and Activity Impairment Questionnaire items contributed most to the predictions. Conclusions This study provides evidence that social-demographic and clinical variables can be used for machine learning to predict therapy outcomes within the context of a therapist-supported digital mental health intervention. Despite the overall moderate performance, this appears promising as these predictions can potentially improve the outcomes of non-responders by monitoring their progress or by offering alternative or additional treatment.
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Affiliation(s)
- Silvan Hornstein
- Meru Health Inc, Palo Alto, CA, USA.,Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | | | | | | | - Kevin Hilbert
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
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11
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Ramos LA, Blankers M, van Wingen G, de Bruijn T, Pauws SC, Goudriaan AE. Predicting Success of a Digital Self-Help Intervention for Alcohol and Substance Use With Machine Learning. Front Psychol 2021; 12:734633. [PMID: 34552539 PMCID: PMC8451420 DOI: 10.3389/fpsyg.2021.734633] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 08/16/2021] [Indexed: 11/13/2022] Open
Abstract
Background Digital self-help interventions for reducing the use of alcohol tobacco and other drugs (ATOD) have generally shown positive but small effects in controlling substance use and improving the quality of life of participants. Nonetheless, low adherence rates remain a major drawback of these digital interventions, with mixed results in (prolonged) participation and outcome. To prevent non-adherence, we developed models to predict success in the early stages of an ATOD digital self-help intervention and explore the predictors associated with participant's goal achievement. Methods We included previous and current participants from a widely used, evidence-based ATOD intervention from the Netherlands (Jellinek Digital Self-help). Participants were considered successful if they completed all intervention modules and reached their substance use goals (i.e., stop/reduce). Early dropout was defined as finishing only the first module. During model development, participants were split per substance (alcohol, tobacco, cannabis) and features were computed based on the log data of the first 3 days of intervention participation. Machine learning models were trained, validated and tested using a nested k-fold cross-validation strategy. Results From the 32,398 participants enrolled in the study, 80% of participants did not complete the first module of the intervention and were excluded from further analysis. From the remaining participants, the percentage of success for each substance was 30% for alcohol, 22% for cannabis and 24% for tobacco. The area under the Receiver Operating Characteristic curve was the highest for the Random Forest model trained on data from the alcohol and tobacco programs (0.71 95%CI 0.69-0.73) and (0.71 95%CI 0.67-0.76), respectively, followed by cannabis (0.67 95%CI 0.59-0.75). Quitting substance use instead of moderation as an intervention goal, initial daily consumption, no substance use on the weekends as a target goal and intervention engagement were strong predictors of success. Discussion Using log data from the first 3 days of intervention use, machine learning models showed positive results in identifying successful participants. Our results suggest the models were especially able to identify participants at risk of early dropout. Multiple variables were found to have high predictive value, which can be used to further improve the intervention.
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Affiliation(s)
- Lucas A Ramos
- Department of Psychiatry, Amsterdam UMC, and Amsterdam Institute for Addiction Research, University of Amsterdam, Amsterdam, Netherlands
| | - Matthijs Blankers
- Department of Psychiatry, Amsterdam UMC, and Amsterdam Institute for Addiction Research, University of Amsterdam, Amsterdam, Netherlands.,Arkin Mental Health Care, Amsterdam, Netherlands.,Trimbos Institute, The Netherlands Institute of Mental Health and Addiction, Utrecht, Netherlands
| | - Guido van Wingen
- Department of Psychiatry, Amsterdam UMC, and Amsterdam Institute for Addiction Research, University of Amsterdam, Amsterdam, Netherlands
| | | | - Steffen C Pauws
- Department of Communication and Cognition, Tilburg University, Tilburg, Netherlands.,Department of Remote Patient Management and Chronic Care, Philips Research, Eindhoven, Netherlands
| | - Anneke E Goudriaan
- Department of Psychiatry, Amsterdam UMC, and Amsterdam Institute for Addiction Research, University of Amsterdam, Amsterdam, Netherlands.,Arkin Mental Health Care, Amsterdam, Netherlands.,Amsterdam Public Health Research Institute, Amsterdam, Netherlands
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12
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Popovic D, Schiltz K, Falkai P, Koutsouleris N. Präzisionspsychiatrie und der Beitrag von Brain Imaging und anderen Biomarkern. FORTSCHRITTE DER NEUROLOGIE-PSYCHIATRIE 2020; 88:778-785. [PMID: 33307561 DOI: 10.1055/a-1300-2162] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
'Precision Psychiatry' as the psychiatric variant of 'Precision Medicine' aims to provide high-level diagnosis and treatment based on robust biomarkers and tailored to the individual clinical, neurobiological, and genetic constitution of the patient. The specific peculiarity of psychiatry, in which disease entities are normatively defined based on clinical experience and are also significantly influenced by contemporary history, society and philosophy, has so far made the search for valid and reliable psychobiological connections difficult. Nevertheless, considerable progress has now been made in all areas of psychiatric research, made possible above all by the critical review and renewal of previous concepts of disease and psychopathology, the increased orientation towards neurobiology and genetics, and in particular the use of machine learning methods. Notably, modern machine learning methods make it possible to integrate high-dimensional and multimodal data sets and generate models which provide new psychobiological insights and offer the possibility of individualized, biomarker-driven single-subject prediction of diagnosis, therapy response and prognosis. The aim of the present review is therefore to introduce the concept of 'Precision Psychiatry' to the interested reader, to concisely present modern, machine learning methods required for this, and to clearly present the current state and future of biomarker-based 'precision psychiatry'.
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Affiliation(s)
- David Popovic
- Klinikum der Universität München, Klinik und Poliklinik für Psychiatrie und Psychotherapie.,International Max Planck Research School for Translational Psychiatry
| | - Kolja Schiltz
- Klinikum der Universität München, Klinik und Poliklinik für Psychiatrie und Psychotherapie
| | - Peter Falkai
- Klinikum der Universität München, Klinik und Poliklinik für Psychiatrie und Psychotherapie.,International Max Planck Research School for Translational Psychiatry
| | - Nikolaos Koutsouleris
- Klinikum der Universität München, Klinik und Poliklinik für Psychiatrie und Psychotherapie.,International Max Planck Research School for Translational Psychiatry
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13
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Symons M, Feeney GFX, Gallagher MR, Young RM, Connor JP. Predicting alcohol dependence treatment outcomes: a prospective comparative study of clinical psychologists versus 'trained' machine learning models. Addiction 2020; 115:2164-2175. [PMID: 32150316 DOI: 10.1111/add.15038] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 10/13/2019] [Accepted: 03/04/2020] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND AIMS 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. DESIGN Prospective study. ML models were trained on 1016 previously treated patients (training-set) who attended a hospital-based alcohol and drug clinic. ML models (n = 27), clinical psychologists (n = 10) and a 'traditional' logistic regression model (n = 1) predicted treatment outcome during the initial treatment session of an alcohol dependence programme. SETTING A 12-week cognitive behavioural therapy (CBT)-based abstinence programme for alcohol dependence in a hospital-based alcohol and drug clinic in Australia. PARTICIPANTS Prospective predictions were made for 220 new patients (test-set; 70.91% male, mean age = 35.78 years, standard deviation = 9.19). Sixty-nine (31.36%) patients successfully completed treatment. MEASUREMENTS Treatment 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. FINDINGS The 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 < 0.05) and had superior calibration. The high specificity for the selected ML (79%) and logistic regression (90%) meant they were significantly (P < 0.001) 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. CONCLUSIONS Machine 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.
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Affiliation(s)
- Martyn Symons
- Alcohol and Drug Assessment Unit, Princess Alexandra Hospital, Brisbane, Australia.,Discipline of Psychiatry, The University of Queensland, Brisbane, Australia.,National Health and Medical Research Council FASD Research Australia Centre of Research Excellence, Telethon Kids Institute, The University of Western Australia, Perth, Australia
| | - Gerald F X Feeney
- Alcohol and Drug Assessment Unit, Princess Alexandra Hospital, Brisbane, Australia.,Centre for Youth Substance Abuse Research, The University of Queensland, Brisbane, Australia
| | - Marcus R Gallagher
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
| | - Ross McD Young
- Alcohol and Drug Assessment Unit, Princess Alexandra Hospital, Brisbane, Australia.,Faculty of Health, Queensland University of Technology, Brisbane, Australia
| | - Jason P Connor
- Alcohol and Drug Assessment Unit, Princess Alexandra Hospital, Brisbane, Australia.,Discipline of Psychiatry, The University of Queensland, Brisbane, Australia.,Centre for Youth Substance Abuse Research, The University of Queensland, Brisbane, Australia
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14
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Richter T, Fishbain B, Markus A, Richter-Levin G, Okon-Singer H. Using machine learning-based analysis for behavioral differentiation between anxiety and depression. Sci Rep 2020; 10:16381. [PMID: 33009424 PMCID: PMC7532220 DOI: 10.1038/s41598-020-72289-9] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2019] [Accepted: 08/28/2020] [Indexed: 01/12/2023] Open
Abstract
Anxiety and depression are distinct—albeit overlapping—psychiatric diseases, currently diagnosed by self-reported-symptoms. This research presents a new diagnostic methodology, which tests rigorously for differences in cognitive biases among subclinical anxious and depressed individuals. 125 participants were divided into four groups based on the levels of their anxiety and depression symptoms. A comprehensive behavioral test battery detected and quantified various cognitive–emotional biases. Advanced machine-learning tools, developed for this study, analyzed these results. These tools detect unique patterns that characterize anxiety versus depression to predict group membership. The prediction model for differentiating between symptomatic participants (i.e., high symptoms of depression, anxiety, or both) compared to the non-symptomatic control group revealed a 71.44% prediction accuracy for the former (sensitivity) and 70.78% for the latter (specificity). 68.07% and 74.18% prediction accuracy was obtained for a two-group model with high depression/anxiety, respectively. The analysis also disclosed which specific behavioral measures contributed to the prediction, pointing to key cognitive mechanisms in anxiety versus depression. These results lay the ground for improved diagnostic instruments and more effective and focused individually-based treatment.
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Affiliation(s)
- Thalia Richter
- Department of Psychology, School of Psychological Sciences, University of Haifa, Mount Carmel, 3498838, Haifa, Israel.
| | - Barak Fishbain
- Faculty of Civil and Environmental Engineering, Technion-Israel Institute of Technology, Haifa, Israel
| | - Andrey Markus
- Department of Psychology, School of Psychological Sciences, University of Haifa, Mount Carmel, 3498838, Haifa, Israel
| | - Gal Richter-Levin
- Department of Psychology, School of Psychological Sciences, University of Haifa, Mount Carmel, 3498838, Haifa, Israel
| | - Hadas Okon-Singer
- Department of Psychology, School of Psychological Sciences, University of Haifa, Mount Carmel, 3498838, Haifa, Israel
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15
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Aafjes-van Doorn K, Kamsteeg C, Bate J, Aafjes M. A scoping review of machine learning in psychotherapy research. Psychother Res 2020; 31:92-116. [PMID: 32862761 DOI: 10.1080/10503307.2020.1808729] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
Abstract
Machine learning (ML) offers robust statistical and probabilistic techniques that can help to make sense of large amounts of data. This scoping review paper aims to broadly explore the nature of research activity using ML in the context of psychological talk therapies, highlighting the scope of current methods and considerations for clinical practice and directions for future research. Using a systematic search methodology, fifty-one studies were identified. A narrative synthesis indicates two types of studies, those who developed and tested an ML model (k=44), and those who reported on the feasibility of a particular treatment tool that uses an ML algorithm (k=7). Most model development studies used supervised learning techniques to classify or predict labeled treatment process or outcome data, whereas others used unsupervised techniques to identify clusters in the unlabeled patient or treatment data. Overall, the current applications of ML in psychotherapy research demonstrated a range of possible benefits for indications of treatment process, adherence, therapist skills and treatment response prediction, as well as ways to accelerate research through automated behavioral or linguistic process coding. Given the novelty and potential of this research field, these proof-of-concept studies are encouraging, however, do not necessarily translate to improved clinical practice (yet).
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Affiliation(s)
| | | | - Jordan Bate
- Ferkauf Graduate School of Psychology, Yeshiva University, Bronx, NY, USA
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16
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Abstract
PURPOSE OF REVIEW To provide an accessible overview of some of the most recent trends in the application of machine learning to the field of substance use disorders and their implications for future research and practice. RECENT FINDINGS Machine-learning (ML) techniques have recently been applied to substance use disorder (SUD) data for multiple predictive applications including detecting current abuse, assessing future risk and predicting treatment success. These models cover a wide range of machine-learning techniques and data types including physiological measures, longitudinal surveys, treatment outcomes, national surveys, medical records and social media. SUMMARY The application of machine-learning models to substance use disorder data shows significant promise, with some use cases and data types showing high predictive accuracy, particularly for models of physiological and behavioral measures for predicting current substance use, portending potential clinical diagnostic applications; however, these results are uneven, with some models performing poorly or at chance, a limitation likely reflecting insufficient data and/or weak validation methods. The field will likely benefit from larger and more multimodal datasets, greater standardization of data recording and rigorous testing protocols as well as greater use of modern deep neural network models applied to multimodal unstructured datasets.
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