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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:

Developing a practical suicide risk prediction model for targeting high‐risk patients in the Veterans health Administration
Ronald C. Kessler, Irving Hwang, Claire A. Hoffmire, et al.
International Journal of Methods in Psychiatric Research (2017) Vol. 26, Iss. 3
Open Access | Times Cited: 173

Showing 1-25 of 173 citing articles:

A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models
Evangelia Christodoulou, Jie Ma, Gary S. Collins, et al.
Journal of Clinical Epidemiology (2019) Vol. 110, pp. 12-22
Open Access | Times Cited: 1377
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
Artificial Intelligence for Mental Health and Mental Illnesses: an Overview
Sarah Graham, Colin A. Depp, Ellen Lee, et al.
Current Psychiatry Reports (2019) Vol. 21, Iss. 11
Open Access | Times Cited: 598
Prediction Models for Suicide Attempts and Deaths
Bradley E. Belsher, Derek J. Smolenski, Larry D. Pruitt, et al.
JAMA Psychiatry (2019) Vol. 76, Iss. 6, pp. 642-642
Closed Access | Times Cited: 411
Predicting Suicide Attempts and Suicide Deaths Following Outpatient Visits Using Electronic Health Records
Gregory E. Simon, Eric Johnson, Jean M. Lawrence, et al.
American Journal of Psychiatry (2018) Vol. 175, Iss. 10, pp. 951-960
Open Access | Times Cited: 328
Understanding Links among Opioid Use, Overdose, and Suicide
Amy S. B. Bohnert, Mark A. Ilgen
New England Journal of Medicine (2019) Vol. 380, Iss. 1, pp. 71-79
Open Access | Times Cited: 274
Smartphones, Sensors, and Machine Learning to Advance Real-Time Prediction and Interventions for Suicide Prevention: a Review of Current Progress and Next Steps
John Torous, Mark Larsen, Colin A. Depp, et al.
Current Psychiatry Reports (2018) Vol. 20, Iss. 7
Closed Access | Times Cited: 187
The use of machine learning in the study of suicidal and non-suicidal self-injurious thoughts and behaviors: A systematic review
Taylor A. Burke, Brooke A. Ammerman, Ross Jacobucci
Journal of Affective Disorders (2018) Vol. 245, pp. 869-884
Closed Access | Times Cited: 175
Suicide prediction models: a critical review of recent research with recommendations for the way forward
Ronald C. Kessler, Robert M. Bossarte, Alex Luedtke, et al.
Molecular Psychiatry (2019) Vol. 25, Iss. 1, pp. 168-179
Open Access | Times Cited: 169
Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations
Rebecca A. Bernert, Amanda M. Hilberg, Ruth Melia, et al.
International Journal of Environmental Research and Public Health (2020) Vol. 17, Iss. 16, pp. 5929-5929
Open Access | Times Cited: 160
How machine-learning recommendations influence clinician treatment selections: the example of antidepressant selection
Maia Jacobs, Melanie F. Pradier, Thomas H. McCoy, et al.
Translational Psychiatry (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 159
Understanding Why Patients May Not Report Suicidal Ideation at a Health Care Visit Prior to a Suicide Attempt: A Qualitative Study
Julie Richards, Ursula Whiteside, Evette Ludman, et al.
Psychiatric Services (2018) Vol. 70, Iss. 1, pp. 40-45
Open Access | Times Cited: 131
Sample Size Requirements for Multivariate Models to Predict Between-Patient Differences in Best Treatments of Major Depressive Disorder
Alex Luedtke, Ekaterina Sadikova, Ronald C. Kessler
Clinical Psychological Science (2019) Vol. 7, Iss. 3, pp. 445-461
Closed Access | Times Cited: 118
Looking to the Future: A Synthesis of New Developments and Challenges in Suicide Research and Prevention
Rory C. O’Connor, Gwendolyn Portzky
Frontiers in Psychology (2018) Vol. 9
Open Access | Times Cited: 116
Identification of Suicide Attempt Risk Factors in a National US Survey Using Machine Learning
Ángel García de la Garza, Carlos Blanco, Mark Olfson, et al.
JAMA Psychiatry (2021) Vol. 78, Iss. 4, pp. 398-398
Open Access | Times Cited: 103
Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records
Le Zheng, Oliver Wang, Shiying Hao, et al.
Translational Psychiatry (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 83
Testing Suicide Risk Prediction Algorithms Using Phone Measurements With Patients in Acute Mental Health Settings: Feasibility Study
Alina Haines‐Delmont, Gurdit Chahal, Ashley Jane Bruen, et al.
JMIR mhealth and uhealth (2020) Vol. 8, Iss. 6, pp. e15901-e15901
Open Access | Times Cited: 74
Natural language processing and machine learning of electronic health records for prediction of first-time suicide attempts
Fuchiang Tsui, Lingyun Shi, Vı́ctor Ruiz, et al.
JAMIA Open (2021) Vol. 4, Iss. 1
Open Access | Times Cited: 74
Natural language processing of clinical mental health notes may add predictive value to existing suicide risk models
Maxwell Levis, Christine Leonard Westgate, Jiang Gui, et al.
Psychological Medicine (2020) Vol. 51, Iss. 8, pp. 1382-1391
Open Access | Times Cited: 72
Evaluation of the Recovery Engagement and Coordination for Health–Veterans Enhanced Treatment Suicide Risk Modeling Clinical Program in the Veterans Health Administration
John F. McCarthy, Samantha A. Cooper, Kallisse R. Dent, et al.
JAMA Network Open (2021) Vol. 4, Iss. 10, pp. e2129900-e2129900
Open Access | Times Cited: 68
The Secondary Use of Electronic Health Records for Data Mining: Data Characteristics and Challenges
Tabinda Sarwar, Sattar Seifollahi, Jeffrey Chan, et al.
ACM Computing Surveys (2022) Vol. 55, Iss. 2, pp. 1-40
Open Access | Times Cited: 67
Translating promise into practice: a review of machine learning in suicide research and prevention
Olivia J Kirtley, Kasper van Mens, Mark Hoogendoorn, et al.
The Lancet Psychiatry (2022) Vol. 9, Iss. 3, pp. 243-252
Open Access | Times Cited: 59
Mapping of machine learning approaches for description, prediction, and causal inference in the social and health sciences
Anja Leist, Matthias Klee, Jung Hyun Kim, et al.
Science Advances (2022) Vol. 8, Iss. 42
Open Access | Times Cited: 58
Artificial intelligence and suicide prevention: A systematic review
Alban Lejeune, Aziliz Le Glaz, Pierre-Antoine Perron, et al.
European Psychiatry (2022) Vol. 65, Iss. 1
Open Access | Times Cited: 55
Digital Interventions for Mental Disorders: Key Features, Efficacy, and Potential for Artificial Intelligence Applications
David Daniel Ebert, Mathias Harrer, Jennifer Apolinário-Hagen, et al.
Advances in experimental medicine and biology (2019), pp. 583-627
Closed Access | Times Cited: 58
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