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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 suicide attempts among Norwegian adolescents without using suicide-related items: a machine learning approach
E. F. Haghish, Nikolai Olavi Czajkowski, Tilmann von Soest
Frontiers in Psychiatry (2023) Vol. 14
Open Access | Times Cited: 12

Showing 12 citing articles:

Analysis and evaluation of explainable artificial intelligence on suicide risk assessment
Hao Tang, Aref Miri Rekavandi, Dharjinder Rooprai, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 6
Suicide attempt risk predicts inconsistent self-reported suicide attempts: A machine learning approach using longitudinal data
E. F. Haghish, Nikolai Olavi Czajkowski, Fredrik A. Walby, et al.
Journal of Affective Disorders (2024) Vol. 355, pp. 495-504
Open Access | Times Cited: 6
Behavioral and social predictors of suicidal ideation and attempts among adolescents and young adults
Yeganeh Shahsavar, Avishek Choudhury
PLOS mental health. (2025) Vol. 2, Iss. 1, pp. e0000221-e0000221
Open Access
Differentiating adolescent suicidal and nonsuicidal self-harm with machine learning: Beyond suicidal intent and capability for suicide
E. F. Haghish
Journal of Affective Disorders (2025)
Open Access
Risk prediction models for adolescent suicide:a systematic review and meta-analysis
Ruitong Li, Yuchuan Yue, Xiaohua Gu, et al.
Psychiatry Research (2025) Vol. 347, pp. 116405-116405
Closed Access
Unveiling sex difference in factors associated with suicide attempt among Chinese adolescents with depression: a machine learning-based study
Yang Wang, Siyu Chen, J Y Liu, et al.
Journal of Mental Health (2025), pp. 1-11
Closed Access
When spirituality fades: a qualitative exploration of spiritual deficiencies in suicide ideation and attempt
Marziyeh Ghahramani, Nadereh Memaryan, Shahrbanoo Ghahari, et al.
Mental Health Religion & Culture (2025), pp. 1-22
Closed Access
Unveiling Adolescent Suicidality: Holistic Analysis of Protective and Risk Factors Using Multiple Machine Learning Algorithms
E. F. Haghish, Ragnhild Bang Nes, Milan Obaidi, et al.
Journal of Youth and Adolescence (2023) Vol. 53, Iss. 3, pp. 507-525
Open Access | Times Cited: 10
Are false positives in suicide classification models a risk group? Evidence for “true alarms” in a population-representative longitudinal study of Norwegian adolescents
E. F. Haghish, Bruno Laeng, Nikolai Olavi Czajkowski
Frontiers in Psychology (2023) Vol. 14
Open Access | Times Cited: 8
Quantifying the importance of factors in predicting non-suicidal self-injury among depressive Chinese adolescents: A comparative study between only child and non-only child groups
Yang Wang, Jie Lin, Zhenzhen Zhu, et al.
Journal of Affective Disorders (2024) Vol. 369, pp. 834-844
Closed Access | Times Cited: 1
Predicting Suicidal Ideation, Planning, and Attempts among the Adolescent Population of the United States
Hamed Khosravi, Imtiaz Ahmed, Avishek Choudhury
Healthcare (2024) Vol. 12, Iss. 13, pp. 1262-1262
Open Access
Optimizing the Use of Artificial Intelligence in Cardiology in 2024
Stephen G. Ellis, Michael W. Kattan
КАРДИОЛОГИЯ УЗБЕКИСТАНА (2024) Vol. 17, Iss. 14, pp. 1717-1718
Closed Access
Do early-life circumstances predict late-life suicidal ideation? Evidence from SHARE data using machine learning
Xuejing Zong, Huaiyue Wang
Frontiers in Psychiatry (2024) Vol. 15
Open Access
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