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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 future suicidal behaviour in young adults, with different machine learning techniques: A population-based longitudinal study
Kasper van Mens, Cwm de Schepper, Ben Wijnen, et al.
Journal of Affective Disorders (2020) Vol. 271, pp. 169-177
Open Access | Times Cited: 56

Showing 1-25 of 56 citing articles:

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
Specific anxiety and depression symptoms are risk factors for the onset of suicidal ideation and suicide attempts in youth
Janie Busby Grant, Philip J. Batterham, Sonia McCallum, et al.
Journal of Affective Disorders (2023) Vol. 327, pp. 299-305
Open Access | Times Cited: 33
Machine Learning Assessment of Early Life Factors Predicting Suicide Attempt in Adolescence or Young Adulthood
Marie C. Navarro, Isabelle Ouellet‐Morin, Marie‐Claude Geoffroy, et al.
JAMA Network Open (2021) Vol. 4, Iss. 3, pp. e211450-e211450
Open Access | Times Cited: 43
Suicidal behaviour prediction models using machine learning techniques: A systematic review
Noratikah Nordin, Zurinahni Zainol, Mohd Halim Mohd Noor, et al.
Artificial Intelligence in Medicine (2022) Vol. 132, pp. 102395-102395
Closed Access | Times Cited: 36
Machine learning-based prediction for self-harm and suicide attempts in adolescents
Raymond Su, James Rufus John, Ping‐I Lin
Psychiatry Research (2023) Vol. 328, pp. 115446-115446
Open Access | Times Cited: 17
Role of machine learning algorithms in suicide risk prediction: a systematic review-meta analysis of clinical studies
Houriyeh Ehtemam, Shabnam Sadeghi Esfahlani, Alireza Sanaei, et al.
BMC Medical Informatics and Decision Making (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 7
Interpersonal theory of suicide: prospective examination
Thomas Forkmann, Heide Glaesmer, Laura Paashaus, et al.
BJPsych Open (2020) Vol. 6, Iss. 5
Open Access | Times Cited: 41
Machine learning for suicidal ideation identification: A systematic literature review
Wesllei Felipe Heckler, Juliano Varella de Carvalho, Jorge Luís Victória Barbosa
Computers in Human Behavior (2021) Vol. 128, pp. 107095-107095
Closed Access | Times Cited: 40
Predicting the 9-year course of mood and anxiety disorders with automated machine learning: A comparison between auto-sklearn, naïve Bayes classifier, and traditional logistic regression
Wessel A. van Eeden, Chuan Luo, Albert M. van Hemert, et al.
Psychiatry Research (2021) Vol. 299, pp. 113823-113823
Open Access | Times Cited: 39
A machine learning approach for predicting suicidal thoughts and behaviours among college students
Mélissa Macalli, Marie C. Navarro, Massimiliano Orri, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 39
Prediction of suicidal ideation among preadolescent children with machine learning models: A longitudinal study
Chi Yang, E. Scott Huebner, Lili Tian
Journal of Affective Disorders (2024) Vol. 352, pp. 403-409
Closed Access | Times Cited: 4
Accuracy of machine learning methods in predicting prognosis of patients with psychotic spectrum disorders: a systematic review
Jing Ling Tay, Yuhao Ang, Wilson Tam, et al.
BMJ Open (2025) Vol. 15, Iss. 2, pp. e084463-e084463
Open Access
Comparing machine learning to a rule-based approach for predicting suicidal behavior among adolescents: Results from a longitudinal population-based survey
C.L. van Vuuren, Kasper van Mens, Derek de Beurs, et al.
Journal of Affective Disorders (2021) Vol. 295, pp. 1415-1420
Open Access | Times Cited: 25
One‐year incidence, predictors, and accuracy of prediction of suicidal thoughts and behaviors from the first to second year of university
Corina Benjet, Guilherme Borges, Sumaiya Miah, et al.
Depression and Anxiety (2022) Vol. 39, Iss. 12, pp. 727-740
Open Access | Times Cited: 16
Machine learning prediction of suicidal ideation, planning, and attempt among Korean adults: A population-based study
Jeongyoon Lee, Tae‐Young Pak
SSM - Population Health (2022) Vol. 19, pp. 101231-101231
Open Access | Times Cited: 16
Machine learning approaches for predicting suicidal behaviors among university students in Bangladesh during the COVID-19 pandemic: A cross-sectional study
Sultan Mahmud, Md Mohsin, Abdul Muyeed, et al.
Medicine (2023) Vol. 102, Iss. 28, pp. e34285-e34285
Open Access | Times Cited: 10
Predicting Lifetime Suicide Attempts in a Community Sample of Adolescents Using Machine Learning Algorithms
Kristin Jankowsky, Diana Steger, Ulrich Schroeders
Assessment (2023) Vol. 31, Iss. 3, pp. 557-573
Open Access | Times Cited: 9
Thoth: An intelligent model for assisting individuals with suicidal ideation
Wesllei Felipe Heckler, Luan Paris Feijó, Juliano Varella de Carvalho, et al.
Expert Systems with Applications (2023) Vol. 233, pp. 120918-120918
Closed Access | Times Cited: 9
Factors Related to Suicidal Ideation by Gender and Age Group in Korean Adults
Eun Young Kim, Yong Whi Jeong, Jihye Lim, et al.
Journal of Korean Medical Science (2023) Vol. 38, Iss. 49
Open Access | Times Cited: 9
Prediction of suicidal ideation with associated risk factors among university students in the southern part of Bangladesh: Machine learning approach
Sabiha Shirin Sara, Md. Asikur Rahman, Riaz Rahman, et al.
Journal of Affective Disorders (2024) Vol. 349, pp. 502-508
Closed Access | Times Cited: 3
Learning from Imbalanced Data in Healthcare: State-of-the-Art and Research Challenges
Debashis Roy, Anandarup Roy, Utpal Roy
Studies in computational intelligence (2024), pp. 19-32
Closed Access | Times Cited: 3
The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors: a systematic review
Nibene Habib Somé, Pardis Noormohammadpour, Shannon Lange
Frontiers in Psychiatry (2024) Vol. 15
Open Access | Times Cited: 3
Bias Discovery in Machine Learning Models for Mental Health
Pablo Mosteiro, Jesse Kuiper, Judith Masthoff, et al.
Information (2022) Vol. 13, Iss. 5, pp. 237-237
Open Access | Times Cited: 15
The role of suicide ideation in assessing near-term suicide risk: A machine learning approach
Lauren McMullen, Neelang Parghi, Megan L. Rogers, et al.
Psychiatry Research (2021) Vol. 304, pp. 114118-114118
Closed Access | Times Cited: 18
5-year incidence of suicide-risk in youth: A gradient tree boosting and SHAP study
Pedro L. Ballester, Taiane de Azevedo Cardoso, Fernanda Pedrotti Moreira, et al.
Journal of Affective Disorders (2021) Vol. 295, pp. 1049-1056
Closed Access | Times Cited: 18
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