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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:

Combining machine learning algorithms for prediction of antidepressant treatment response
Alexander Kautzky, Hans‐Juergen Möller, Markus Dold, et al.
Acta Psychiatrica Scandinavica (2020) Vol. 143, Iss. 1, pp. 36-49
Open Access | Times Cited: 35

Showing 1-25 of 35 citing articles:

Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review
Roberto Tornero-Costa, Antonio Martínez-Millana, Natasha Azzopardi‐Muscat, et al.
JMIR Mental Health (2023) Vol. 10, pp. e42045-e42045
Open Access | Times Cited: 54
AI-assisted prediction of differential response to antidepressant classes using electronic health records
Yi-han Sheu, Colin Magdamo, Matthew Miller, et al.
npj Digital Medicine (2023) Vol. 6, Iss. 1
Open Access | Times Cited: 44
Potential applications and performance of machine learning techniques and algorithms in clinical practice: A systematic review
Ezekwesiri Michael Nwanosike, Barbara R. Conway, Hamid A. Merchant, et al.
International Journal of Medical Informatics (2021) Vol. 159, pp. 104679-104679
Open Access | Times Cited: 81
A factor analytic comparison of three commonly used depression scales (HAMD, MADRS, BDI) in a large sample of depressed inpatients
Florian Seemüller, Rebecca Schennach, Richard Musil, et al.
BMC Psychiatry (2023) Vol. 23, Iss. 1
Open Access | Times Cited: 26
Pursuing personalized medicine for depression by targeting the lateral or medial prefrontal cortex with Deep TMS
Abraham Zangen, Samuel Zibman, Aron Tendler, et al.
JCI Insight (2023) Vol. 8, Iss. 4
Open Access | Times Cited: 20
Prediction of treatment response in major depressive disorder using a hybrid of convolutional recurrent deep neural networks and effective connectivity based on EEG signal
Seyed Morteza Mirjebreili, Reza Shalbaf, Ahmad Shalbaf
Physical and Engineering Sciences in Medicine (2024) Vol. 47, Iss. 2, pp. 633-642
Open Access | Times Cited: 6
A convolutional recurrent neural network with attention for response prediction to repetitive transcranial magnetic stimulation in major depressive disorder
Mohsen Sadat Shahabi, Ahmad Shalbaf, Reza Rostami, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 11
Machine learning for antidepressant treatment selection in depression
Prehm I.M. Arnold, Joost Janzing, Arjen Hommersom
Drug Discovery Today (2024) Vol. 29, Iss. 8, pp. 104068-104068
Open Access | Times Cited: 4
Attention-Based Convolutional Recurrent Deep Neural Networks for the Prediction of Response to Repetitive Transcranial Magnetic Stimulation for Major Depressive Disorder
Mohsen Sadat Shahabi, Ahmad Shalbaf, Behrooz Nobakhsh, et al.
International Journal of Neural Systems (2022) Vol. 33, Iss. 02
Closed Access | Times Cited: 16
Effectiveness of artificial intelligence in detecting and managing depressive disorders: Systematic review
Yoonseo Park, Sewon Park, Munjae Lee
Journal of Affective Disorders (2024) Vol. 361, pp. 445-456
Closed Access | Times Cited: 3
Comparison of traditional model-based statistical methods with machine learning for the prediction of suicide behaviour
Leandro Nicolás Grendas, Luciana Carla Chiapella, Demián Rodante, et al.
Journal of Psychiatric Research (2021) Vol. 145, pp. 85-91
Closed Access | Times Cited: 23
Machine learning, pharmacogenomics, and clinical psychiatry: predicting antidepressant response in patients with major depressive disorder
William V. Bobo, Bailey Van Ommeren, Arjun P. Athreya
Expert Review of Clinical Pharmacology (2022) Vol. 15, Iss. 8, pp. 927-944
Closed Access | Times Cited: 14
Beneficial effects of Silexan on co-occurring depressive symptoms in patients with subthreshold anxiety and anxiety disorders: randomized, placebo-controlled trials revisited
Lucie Bartova, Markus Dold, Hans‐Peter Volz, et al.
European Archives of Psychiatry and Clinical Neuroscience (2022) Vol. 273, Iss. 1, pp. 51-63
Open Access | Times Cited: 12
Development and validation of a machine learning-based vocal predictive model for major depressive disorder
Yael Wasserzug, Yoav Degani, Mili Bar-Shaked, et al.
Journal of Affective Disorders (2022) Vol. 325, pp. 627-632
Closed Access | Times Cited: 11
Development of a multivariate prediction model for antidepressant resistant depression using reward-related predictors
Xiao Liu, Stephen Read
Frontiers in Psychiatry (2024) Vol. 15
Open Access | Times Cited: 2
Predictors of 4-week antidepressant outcome in patients with first-episode major depressive disorder: An ROC curve analysis
Yanling Zhou, Zhipei Zhang, Chengyu Wang, et al.
Journal of Affective Disorders (2022) Vol. 304, pp. 59-65
Closed Access | Times Cited: 8
The pharmacogenomics of selective serotonin reuptake inhibitors
Luis M. García‐Marín, Jill A. Rabinowitz, Zuriel Ceja, et al.
Pharmacogenomics (2022) Vol. 23, Iss. 10, pp. 597-607
Closed Access | Times Cited: 8
Artificial Intelligence and the Medicine of the Future
Richard Woodman, Arduino Alexander Mangoni
Practical issues in geriatrics (2023), pp. 175-204
Closed Access | Times Cited: 4
Factors Associated with Antidepressant Effects of Ketamine: A Reanalysis of Double-Blind Randomized Placebo-Controlled Trial of Intravenous Ketamine for Treatment-Resistant Depression
Kengo Yonezawa, Hiroyuki Uchida, Taisuke Yatomi, et al.
Pharmacopsychiatry (2023) Vol. 57, Iss. 01, pp. 35-40
Open Access | Times Cited: 4
Machine Learning-Based Definition of Symptom Clusters and Selection of Antidepressants for Depressive Syndrome
Il Bin Kim, Seon‐Cheol Park
Diagnostics (2021) Vol. 11, Iss. 9, pp. 1631-1631
Open Access | Times Cited: 9
Charting the dorsal-medial functional gradient of the default mode network in major depressive disorder
Yang Xiao, Donglin Wang, Zhonglin Tan, et al.
Journal of Psychiatric Research (2022) Vol. 153, pp. 1-10
Closed Access | Times Cited: 6
Integrating machine learning with electronic health record data to facilitate detection of prolactin level and pharmacovigilance signals in olanzapine-treated patients
Xiuqing Zhu, Jinqing Hu, Tao Xiao, et al.
Frontiers in Endocrinology (2022) Vol. 13
Open Access | Times Cited: 6
PRimary carE digital Support ToOl in mental health (PRESTO): Design, development and study protocols
Gerard Anmella, Mireia Primé‐Tous, Xavier Segú, et al.
Spanish Journal of Psychiatry and Mental Health (2021) Vol. 17, Iss. 2, pp. 114-125
Closed Access | Times Cited: 8
Development and validation of machine learning model for predicting treatment responders in patients with primary biliary cholangitis
Naruhiro Kimura, Kazuya Takahashi, Toru Setsu, et al.
Hepatology Research (2023) Vol. 54, Iss. 1, pp. 67-77
Closed Access | Times Cited: 3
Examining Stress and Residual Symptoms in Remitted and Partially Remitted Depression Using a Wearable Electrodermal Activity Device: A Pilot Study
Aoife Whiston, Eric R. Igou, Dónal G. Fortune, et al.
IEEE Journal of Translational Engineering in Health and Medicine (2022) Vol. 11, pp. 96-106
Open Access | Times Cited: 4
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