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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:

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

Showing 1-25 of 175 citing articles:

An explainable predictive model for suicide attempt risk using an ensemble learning and Shapley Additive Explanations (SHAP) approach
Noratikah Nordin, Zurinahni Zainol, Mohd Halim Mohd Noor, et al.
Asian Journal of Psychiatry (2022) Vol. 79, pp. 103316-103316
Closed Access | Times Cited: 72
Machine Learning–Based Prediction of Suicidality in Adolescents With Allergic Rhinitis: Derivation and Validation in 2 Independent Nationwide Cohorts
Hojae Lee, Joong Ki Cho, Jaeyu Park, et al.
Journal of Medical Internet Research (2024) Vol. 26, pp. e51473-e51473
Open Access | Times Cited: 22
Machine Learning and Psychological Research: The Unexplored Effect of Measurement
Ross Jacobucci, Kevin J. Grimm
Perspectives on Psychological Science (2020) Vol. 15, Iss. 3, pp. 809-816
Open Access | Times Cited: 114
Data mining and machine learning techniques applied to public health problems: A bibliometric analysis from 2009 to 2018
Bruno Samways dos Santos, María Teresinha Arns Steiner, Amanda Trojan Fenerich, et al.
Computers & Industrial Engineering (2019) Vol. 138, pp. 106120-106120
Closed Access | Times Cited: 108
Prevalence, Onset, and Course of Suicidal Behavior Among Adolescents and Young Adults in Germany
Catharina Voß, Theresa Magdalena Ollmann, Marcel Miché, et al.
JAMA Network Open (2019) Vol. 2, Iss. 10, pp. e1914386-e1914386
Open Access | Times Cited: 100
Machine learning for suicide risk prediction in children and adolescents with electronic health records
Chang Su, Robert H. Aseltine, Riddhi Doshi, et al.
Translational Psychiatry (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 96
Prediction models for high risk of suicide in Korean adolescents using machine learning techniques
Jun Su Jung, Sung Jin Park, Eun Young Kim, et al.
PLoS ONE (2019) Vol. 14, Iss. 6, pp. e0217639-e0217639
Open Access | Times Cited: 79
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
A direct comparison of theory-driven and machine learning prediction of suicide: A meta-analysis
Katherine Musacchio Schafer, Grace Kennedy, Austin J. Gallyer, et al.
PLoS ONE (2021) Vol. 16, Iss. 4, pp. e0249833-e0249833
Open Access | Times Cited: 57
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
Can we predict who will benefit from cognitive-behavioural therapy? A systematic review and meta-analysis of machine learning studies
Sandra Vieira, Xinyi Liang, Raquel Guiomar, et al.
Clinical Psychology Review (2022) Vol. 97, pp. 102193-102193
Open Access | Times Cited: 49
Prediction of non-suicidal self-injury in adolescents at the family level using regression methods and machine learning
Si Chen Zhou, Zhaohe Zhou, Qi Tang, et al.
Journal of Affective Disorders (2024) Vol. 352, pp. 67-75
Closed Access | Times Cited: 12
Supervised machine learning for exploratory analysis in family research
Xiaoran Sun
Journal of Marriage and Family (2024) Vol. 86, Iss. 5, pp. 1468-1494
Open Access | Times Cited: 9
Social media use and deliberate self-harm among youth: A systematized narrative review
Candice Biernesser, Craig Sewall, David A. Brent, et al.
Children and Youth Services Review (2020) Vol. 116, pp. 105054-105054
Open Access | Times Cited: 57
Machine learning enhances prediction of illness course: a longitudinal study in eating disorders
Ann F. Haynos, Shirley B. Wang, Sarah M. Lipson, et al.
Psychological Medicine (2020) Vol. 51, Iss. 8, pp. 1392-1402
Open Access | Times Cited: 56
Prediction of suicidal ideation and attempt in 9 and 10 year-old children using transdiagnostic risk features
Gareth Harman, Dakota Kliamovich, Angelica M. Morales, et al.
PLoS ONE (2021) Vol. 16, Iss. 5, pp. e0252114-e0252114
Open Access | Times Cited: 55
Impact of Big Data Analytics on People’s Health: Overview of Systematic Reviews and Recommendations for Future Studies
Israel Júnior Borges do Nascimento, Milena Soriano Marcolino, Hebatullah Mohamed Abdulazeem, et al.
Journal of Medical Internet Research (2021) Vol. 23, Iss. 4, pp. e27275-e27275
Open Access | Times Cited: 54
Evidence of Inflated Prediction Performance: A Commentary on Machine Learning and Suicide Research
Ross Jacobucci, Andrew K. Littlefield, Alexander J. Millner, et al.
Clinical Psychological Science (2021) Vol. 9, Iss. 1, pp. 129-134
Closed Access | Times Cited: 53
Applying Machine Learning Approaches to Suicide Prediction Using Healthcare Data: Overview and Future Directions
Edwin D. Boudreaux, Elke A. Rundensteiner, Feifan Liu, et al.
Frontiers in Psychiatry (2021) Vol. 12
Open Access | Times Cited: 45
Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews
Antonio Martínez-Millana, Aida Saez-Saez, Roberto Tornero-Costa, et al.
International Journal of Medical Informatics (2022) Vol. 166, pp. 104855-104855
Open Access | Times Cited: 37
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
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
Predicting Children with ADHD Using Behavioral Activity: A Machine Learning Analysis
Md. Maniruzzaman, Jungpil Shin, Md. Al Mehedi Hasan
Applied Sciences (2022) Vol. 12, Iss. 5, pp. 2737-2737
Open Access | Times Cited: 29
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
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