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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Predicting alcohol dependence treatment outcomes: a prospective comparative study of clinical psychologists versus ‘trained’ machine learning models
Martyn Symons, Gerald F.X. Feeney, Marcus Gallagher, et al.
Addiction (2020) Vol. 115, Iss. 11, pp. 2164-2175
Closed Access | Times Cited: 23

Showing 23 citing articles:

Can we predict who will benefit from cognitive-behavioural therapy? A systematic review and meta-analysis of machine learning studies
Sandra Vieira, Xinyi Liang, Raquel Guiomar, et al.
Clinical Psychology Review (2022) Vol. 97, pp. 102193-102193
Open Access | Times Cited: 47
The impact of artificial intelligence on the tasks of mental healthcare workers: A scoping review
Ana Daniela Peres Rebelo, D. E. Verboom Damion, Nuno Rebelo dos Santos, et al.
Computers in Human Behavior Artificial Humans (2023) Vol. 1, Iss. 2, pp. 100008-100008
Open Access | Times Cited: 16
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
Kevin Hilbert, Joscha Böhnlein, Charlotte Meinke, et al.
NeuroImage (2024) Vol. 295, pp. 120639-120639
Open Access | Times Cited: 5
Design and methods of the research unit 5187 PREACT (towards precision psychotherapy for non-respondent patients: from signatures to predictions to clinical utility) – a study protocol for a multicentre observational study in outpatient clinics
Till Langhammer, Chantal Unterfeld, Felix Blankenburg, et al.
BMJ Open (2025) Vol. 15, Iss. 2, pp. e094110-e094110
Open Access
On exploring features engineering and machine learning algorithms in the domain of clinical-psychological
Baiyang Liu, Da He, Li Jia, et al.
Alexandria Engineering Journal (2025) Vol. 123, pp. 488-496
Closed Access
What the future holds: Machine learning to predict success in psychotherapy
Friedrich-Samuel Taubitz, Björn Büdenbender, Georg W. Alpers
Behaviour Research and Therapy (2022) Vol. 156, pp. 104116-104116
Closed Access | Times Cited: 16
Learning algorithms estimate pose and detect motor anomalies in flies exposed to minimal doses of a toxicant
Gianluca Manduca, Valeria Zeni, Sara Moccia, et al.
iScience (2023) Vol. 26, Iss. 12, pp. 108349-108349
Open Access | Times Cited: 7
Computational models of exploration and exploitation characterise onset and efficacy of treatment in methamphetamine use disorder
Alex H. Robinson, Trevor T.‐J. Chong, Antonio Verdejo‐García
Addiction Biology (2022) Vol. 27, Iss. 3
Open Access | Times Cited: 12
Using Machine Learning to Predict Treatment Adherence in Patients on Medication for Opioid Use Disorder
Albert J. Burgess-Hull, Caleb S. Brooks, David H. Epstein, et al.
Journal of Addiction Medicine (2022)
Closed Access | Times Cited: 11
Using machine learning to predict heavy drinking during outpatient alcohol treatment
Walter Roberts, Yize Zhao, Terril L. Verplaetse, et al.
Alcoholism Clinical and Experimental Research (2022) Vol. 46, Iss. 4, pp. 657-666
Open Access | Times Cited: 9
Clinical judgment shows similar and sometimes superior discrimination compared to prognostic clinical prediction models: a systematic review
Luis Enrique Colunga‐Lozano, Farid Foroutan, Daniel Rayner, et al.
Journal of Clinical Epidemiology (2023) Vol. 165, pp. 111200-111200
Closed Access | Times Cited: 5
A cohort of patients in New York State with an alcohol use disorder and subsequent treatment information – A merging of two administrative data sources
Chi‐Hua Lu, Gail Jette, Zackary Falls, et al.
Journal of Biomedical Informatics (2023) Vol. 144, pp. 104443-104443
Open Access | Times Cited: 4
Who responds to a multi‐component treatment for cannabis use disorder? Using multivariable and machine learning models to classify treatment responders and non‐responders
Rachel L. Tomko, Bethany J. Wolf, Erin A. McClure, et al.
Addiction (2023) Vol. 118, Iss. 10, pp. 1965-1974
Closed Access | Times Cited: 2
Machine Learning Applications and Advancements in Alcohol Use Disorder: A Systematic Review
Myrna Hurtado, Anna Siefkas, Misty M. Attwood, et al.
medRxiv (Cold Spring Harbor Laboratory) (2022)
Open Access | Times Cited: 4
Predicting Substance Use Treatment Failure with Transfer Learning
Jordan D. Bailey, Anthony DeFulio
Substance Use & Misuse (2022) Vol. 57, Iss. 13, pp. 1982-1987
Closed Access | Times Cited: 4
Prediction of Cocaine Inpatient Treatment Success Using Machine Learning on High-Dimensional Heterogeneous Data
José Antonio Rivera Tapia, Jose M. Iniesta-Chamorro, Carmen Pérez-Gandía, et al.
IEEE Access (2020) Vol. 8, pp. 218936-218953
Open Access | Times Cited: 4
A Novel Method for Predicting Time of Alcohol Use Based on Personality Traits and Demographic Information
Divya Kumari, Aleena Swetapadma
IETE Journal of Research (2022) Vol. 69, Iss. 11, pp. 7846-7855
Closed Access | Times Cited: 3
Visualizing Model Behaviors for Clinic Users: Explaining A Clinical Prediction Model for 30-day Readmission after Inpatient Alcohol Dependence Treatment
Ou Stella Liang, Christopher C. Yang, Kate Gliske, et al.
2022 IEEE 10th International Conference on Healthcare Informatics (ICHI) (2024), pp. 718-724
Closed Access
Craving for a Robust Methodology: A Systematic Review of Machine Learning Algorithms on Substance-Use Disorders Treatment Outcomes
Bernardo Paim de Mattos, Christian Mattjie, Rafaela Ravazio, et al.
International Journal of Mental Health and Addiction (2024)
Open Access
Comparison between clinician and machine learning prediction in a randomized controlled trial for nonsuicidal self-injury
Moa Pontén, Oskar Flygare, Martin Bellander, et al.
BMC Psychiatry (2024) Vol. 24, Iss. 1
Open Access
Personalized psychotherapy of posttraumatic stress disorder
Carolin Steuwe, Jakob Blaß, Sabine C. Herpertz, et al.
Der Nervenarzt (2023) Vol. 94, Iss. 11, pp. 1050-1058
Open Access
Predicting Therapy Outcome in a Digital Mental Health Intervention for Depression and Anxiety: A Machine Learning Approach (Preprint)
Silvan Hornstein, Valerie L. Forman‐Hoffman, Albert Nazander, et al.
(2020)
Open Access
Development of the motivational thought frequency scale for alcohol abstinence (MTF‐A)
Brandon Cheng, Jason M. Coates, Matthew J. Gullo, et al.
Alcoholism Clinical and Experimental Research (2022) Vol. 46, Iss. 11, pp. 2077-2088
Closed Access
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