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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:

Prediction of Sex-Specific Suicide Risk Using Machine Learning and Single-Payer Health Care Registry Data From Denmark
Jaimie L. Gradus, Anthony J. Rosellini, Erzsébet Horváth‐Puhó, et al.
JAMA Psychiatry (2019) Vol. 77, Iss. 1, pp. 25-25
Open Access | Times Cited: 121

Showing 1-25 of 121 citing articles:

Supervised Machine Learning: A Brief Primer
Tammy Jiang, Jaimie L. Gradus, Anthony J. Rosellini
Behavior Therapy (2020) Vol. 51, Iss. 5, pp. 675-687
Open Access | Times Cited: 505
Machine learning and the prediction of suicide in psychiatric populations: a systematic review
Alessandro Pigoni, Giuseppe Delvecchio, Nunzio Turtulici, et al.
Translational Psychiatry (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 20
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
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: 54
Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review
Yixian Xu, Xinkai Zheng, Yuanjie Li, et al.
Journal of Evidence-Based Medicine (2023) Vol. 16, Iss. 3, pp. 342-375
Open Access | Times Cited: 33
The Performance of Non-pns Islamic Religious Exechers With Work Environment on Work Satisfaction of Employees of the Ministry of Religion Office in Kua Simokerto District, Surabaya City
Aqib Zarnuji
Journal International Dakwah and Communication (2023) Vol. 3, Iss. 1, pp. 33-42
Open Access | Times Cited: 23
Predicting suicide attempt or suicide death following a visit to psychiatric specialty care: A machine learning study using Swedish national registry data
Qi Chen, Yanli Zhang‐James, Eric J. Barnett, et al.
PLoS Medicine (2020) Vol. 17, Iss. 11, pp. e1003416-e1003416
Open Access | Times Cited: 57
Suicide risk assessment in UK mental health services: a national mixed-methods study
Jane Graney, Isabelle M. Hunt, Leah Quinlivan, et al.
The Lancet Psychiatry (2020) Vol. 7, Iss. 12, pp. 1046-1053
Open Access | Times Cited: 54
Prospective Validation of an Electronic Health Record–Based, Real-Time Suicide Risk Model
Colin G. Walsh, Kevin B. Johnson, Michael Ripperger, et al.
JAMA Network Open (2021) Vol. 4, Iss. 3, pp. e211428-e211428
Open Access | Times Cited: 54
Can machine-learning methods really help predict suicide?
Catherine McHugh, Matthew Large
Current Opinion in Psychiatry (2020) Vol. 33, Iss. 4, pp. 369-374
Closed Access | Times Cited: 50
Machine learning, artificial intelligence, and data science breaking into drug design and neglected diseases
José Peña‐Guerrero, Paul Nguewa, Alfonso T. García‐Sosa
Wiley Interdisciplinary Reviews Computational Molecular Science (2021) Vol. 11, Iss. 5
Closed Access | Times Cited: 49
The performance of machine learning models in predicting suicidal ideation, attempts, and deaths: A meta-analysis and systematic review
Karen Kusuma, Mark Larsen, Juan C. Quiroz, et al.
Journal of Psychiatric Research (2022) Vol. 155, pp. 579-588
Closed Access | Times Cited: 35
Complex modeling with detailed temporal predictors does not improve health records-based suicide risk prediction
Susan M. Shortreed, Rod Walker, Eric Johnson, et al.
npj Digital Medicine (2023) Vol. 6, Iss. 1
Open Access | Times Cited: 16
The compatibility of theoretical frameworks with machine learning analyses in psychological research
Jon D. Elhai, Christian Montag
Current Opinion in Psychology (2020) Vol. 36, pp. 83-88
Closed Access | Times Cited: 43
Just in time crisis response: suicide alert system for telemedicine psychotherapy settings
Niels Bantilan, Matteo Malgaroli, Bonnie K. Ray, et al.
Psychotherapy Research (2020) Vol. 31, Iss. 3, pp. 289-299
Closed Access | Times Cited: 40
Identifying socio-demographic risk factors for suicide using data on an individual level
Guus Berkelmans, Rob van der Mei, Sandjai Bhulai, et al.
BMC Public Health (2021) Vol. 21, Iss. 1
Open Access | Times Cited: 36
County-level estimates of suicide mortality in the USA: a modelling study
Sasikiran Kandula, Gonzalo Martínez‐Alés, Caroline Rutherford, et al.
The Lancet Public Health (2023) Vol. 8, Iss. 3, pp. e184-e193
Open Access | Times Cited: 13
Exploring risk factors and their differences on suicidal ideation and suicide attempts among depressed adolescents based on decision tree model
Yang Wang, J Y Liu, Siyu Chen, et al.
Journal of Affective Disorders (2024) Vol. 352, pp. 87-100
Closed Access | Times Cited: 5
The potential role of machine learning and deep learning in differential diagnosis of Alzheimer’s disease and FTD using imaging biomarkers: A review
Sara Mirabian, Fatemeh Mohammadian, Zohreh Ganji, et al.
The Neuroradiology Journal (2025)
Open Access
Meta-analysis of the strength of exploratory suicide prediction models; from clinicians to computers
Michelle Corke, Katherine Mullin, Helena N. Angel-Scott, et al.
BJPsych Open (2021) Vol. 7, Iss. 1
Open Access | Times Cited: 31
Machine Learning to Differentiate Risk of Suicide Attempt and Self-harm After General Medical Hospitalization of Women With Mental Illness
Juliet Beni Edgcomb, Rohith Thiruvalluru, Jyotishman Pathak, et al.
Medical Care (2021) Vol. 59, pp. S58-S64
Open Access | Times Cited: 28
Validation of a Multivariable Model to Predict Suicide Attempt in a Mental Health Intake Sample
Santiago Papini, Honor Hsin, Patricia Kipnis, et al.
JAMA Psychiatry (2024) Vol. 81, Iss. 7, pp. 700-700
Closed Access | Times Cited: 4
A survey on detecting mental disorders with natural language processing: Literature review, trends and challenges
Arturo Montejo‐Ráez, M. Dolores Molina-González, Salud María Jiménez-Zafra, et al.
Computer Science Review (2024) Vol. 53, pp. 100654-100654
Open Access | Times Cited: 4
Machine Learning for Suicide Research–Can It Improve Risk Factor Identification?
Seena Fazel, Lauren M. O’Reilly
JAMA Psychiatry (2019) Vol. 77, Iss. 1, pp. 13-13
Open Access | Times Cited: 34
Addressing Measurement Error in Random Forests Using Quantitative Bias Analysis
Tammy Jiang, Jaimie L. Gradus, Timothy L. Lash, et al.
American Journal of Epidemiology (2021) Vol. 190, Iss. 9, pp. 1830-1840
Open Access | Times Cited: 26
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