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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:

Early identification of posttraumatic stress following military deployment: Application of machine learning methods to a prospective study of Danish soldiers
Karen‐Inge Karstoft, Alexander Statnikov, Søren Bo Andersen, et al.
Journal of Affective Disorders (2015) Vol. 184, pp. 170-175
Closed Access | Times Cited: 71

Showing 1-25 of 71 citing articles:

Machine learning in mental health: a scoping review of methods and applications
Adrian Shatte, Delyse Hutchinson, Samantha Teague
Psychological Medicine (2019) Vol. 49, Iss. 09, pp. 1426-1448
Open Access | Times Cited: 689
Stress and Fear Extinction
Stephen Maren, Andrew Holmes
Neuropsychopharmacology (2015) Vol. 41, Iss. 1, pp. 58-79
Open Access | Times Cited: 369
Implementing Precision Psychiatry: A Systematic Review of Individualized Prediction Models for Clinical Practice
Gonzalo Salazar de Pablo, Erich Studerus, Julio Vaquerizo‐Serrano, et al.
Schizophrenia Bulletin (2020) Vol. 47, Iss. 2, pp. 284-297
Open Access | Times Cited: 162
Computational neuroscience approach to biomarkers and treatments for mental disorders
Noriaki Yahata, Kiyoto Kasai, Mitsuo Kawato
Psychiatry and Clinical Neurosciences (2016) Vol. 71, Iss. 4, pp. 215-237
Open Access | Times Cited: 112
Machine Learning for Predicting Outcomes in Trauma
Nehemiah T. Liu, José Salinas
Shock (2017) Vol. 48, Iss. 5, pp. 504-510
Closed Access | Times Cited: 104
Pre-deployment risk factors for PTSD in active-duty personnel deployed to Afghanistan: a machine-learning approach for analyzing multivariate predictors
Katharina Schultebraucks, Meng Qian, Duna Abu‐Amara, et al.
Molecular Psychiatry (2020) Vol. 26, Iss. 9, pp. 5011-5022
Open Access | Times Cited: 90
The Current Evidence for Acute Stress Disorder
Richard A. Bryant
Current Psychiatry Reports (2018) Vol. 20, Iss. 12
Closed Access | Times Cited: 85
The use of machine learning techniques in trauma-related disorders: a systematic review
Luís Francisco Ramos-Lima, Vitória Waikamp, Thyago Antonelli-Salgado, et al.
Journal of Psychiatric Research (2019) Vol. 121, pp. 159-172
Closed Access | Times Cited: 82
Applications of artificial intelligence−machine learning for detection of stress: a critical overview
Alexios‐Fotios A. Mentis, Donghoon Lee, Panos Roussos
Molecular Psychiatry (2023) Vol. 29, Iss. 6, pp. 1882-1894
Closed Access | Times Cited: 35
Identifying probable post-traumatic stress disorder: applying supervised machine learning to data from a UK military cohort
Daniel Leightley, Victoria Williamson, John Darby, et al.
Journal of Mental Health (2018) Vol. 28, Iss. 1, pp. 34-41
Open Access | Times Cited: 74
Predicting posttraumatic stress disorder following a natural disaster
Anthony J. Rosellini, Francisca Dussaillant, José R. Zubizarreta, et al.
Journal of Psychiatric Research (2017) Vol. 96, pp. 15-22
Open Access | Times Cited: 67
e-PTSD: an overview on how new technologies can improve prediction and assessment of Posttraumatic Stress Disorder (PTSD)
Alexis Bourla, Stéphane Mouchabac, Wissam El‐Hage, et al.
European journal of psychotraumatology (2018) Vol. 9, Iss. sup1
Open Access | Times Cited: 66
Machine Learning for Prediction of Posttraumatic Stress and Resilience Following Trauma: An Overview of Basic Concepts and Recent Advances
Katharina Schultebraucks, Isaac R. Galatzer‐Levy
Journal of Traumatic Stress (2019) Vol. 32, Iss. 2, pp. 215-225
Closed Access | Times Cited: 66
Development and Validation of a Machine Learning Prediction Model of Posttraumatic Stress Disorder After Military Deployment
Santiago Papini, Sonya B. Norman, Laura Campbell‐Sills, et al.
JAMA Network Open (2023) Vol. 6, Iss. 6, pp. e2321273-e2321273
Open Access | Times Cited: 17
Predictors of recovery from post-traumatic stress disorder after the dongting lake flood in China: a 13–14 year follow-up study
Wenjie Dai, Jieru Wang, Atipatsa Chiwanda Kaminga, et al.
BMC Psychiatry (2016) Vol. 16, Iss. 1
Open Access | Times Cited: 53
Tempering aversive/traumatic memories with cannabinoids: a review of evidence from animal and human studies
Sabrina F. Lisboa, Carla Vila-Verde, Jéssica Rosa, et al.
Psychopharmacology (2019) Vol. 236, Iss. 1, pp. 201-226
Closed Access | Times Cited: 48
Using Artificial Neural Networks in Predicting the Level of Stress among Military Conscripts
Svajonė Bekešienė, Rasa Smaliukienė, Ramutė Vaičaitienė
Mathematics (2021) Vol. 9, Iss. 6, pp. 626-626
Open Access | Times Cited: 34
Military Applications of Machine Learning: A Bibliometric Perspective
José Javier Galán-Hernández, R.A. Carrasco, Antonio LaTorre
Mathematics (2022) Vol. 10, Iss. 9, pp. 1397-1397
Open Access | Times Cited: 23
A survey on AI and decision support systems in psychiatry – Uncovering a dilemma
Markus Bertl, Peeter Ross, Dirk Draheim
Expert Systems with Applications (2022) Vol. 202, pp. 117464-117464
Closed Access | Times Cited: 23
Using Neural Networks with Routine Health Records to Identify Suicide Risk: Feasibility Study
Marcos DelPozo‐Baños, Ann John, Nicolai Petkov, et al.
JMIR Mental Health (2018) Vol. 5, Iss. 2, pp. e10144-e10144
Open Access | Times Cited: 46
Data Science in the Research Domain Criteria Era: Relevance of Machine Learning to the Study of Stress Pathology, Recovery, and Resilience
Isaac R. Galatzer‐Levy, Kelly V. Ruggles, Zhe Chen
Chronic Stress (2018) Vol. 2
Open Access | Times Cited: 40
Machine-learning-based classification between post-traumatic stress disorder and major depressive disorder using P300 features
Miseon Shim, Min Jin Jin, Chang‐Hwan Im, et al.
NeuroImage Clinical (2019) Vol. 24, pp. 102001-102001
Open Access | Times Cited: 39
AI enabled suicide prediction tools: a qualitative narrative review
Daniel D’Hotman, Erwin Loh
BMJ Health & Care Informatics (2020) Vol. 27, Iss. 3, pp. e100175-e100175
Open Access | Times Cited: 38
A systematic literature review of AI-based digital decision support systems for post-traumatic stress disorder
Markus Bertl, Janek Metsallik, Peeter Ross
Frontiers in Psychiatry (2022) Vol. 13
Open Access | Times Cited: 20
A systematic review of machine learning findings in PTSD and their relationships with theoretical models
Wivine Blekić, Fabien D’Hondt, Arieh Y. Shalev, et al.
Nature Mental Health (2025) Vol. 3, Iss. 1, pp. 139-158
Closed Access
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