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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:

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 76-100 of 173 citing articles:

Reaching Those at Highest Risk for Suicide: Development of a Model Using Machine Learning Methods for use With Native American Communities
Emily E. Haroz, Colin G. Walsh, Novalene Goklish, et al.
Suicide and Life-Threatening Behavior (2019) Vol. 50, Iss. 2, pp. 422-436
Open Access | Times Cited: 23
Using machine learning to predict suicide in the 30 days after discharge from psychiatric hospital in Denmark
Tammy Jiang, Anthony J. Rosellini, Erzsébet Horváth‐Puhó, et al.
The British Journal of Psychiatry (2021) Vol. 219, Iss. 2, pp. 440-447
Open Access | Times Cited: 20
Designing a Clinical Decision Support Tool That Leverages Machine Learning for Suicide Risk Prediction: Development Study in Partnership With Native American Care Providers
Emily E. Haroz, Fiona Grubin, Novalene Goklish, et al.
JMIR Public Health and Surveillance (2021) Vol. 7, Iss. 9, pp. e24377-e24377
Open Access | Times Cited: 19
An implementation evaluation of “Zero Suicide” using normalization process theory to support high-quality care for patients at risk of suicide
Julie Richards, Gregory E. Simon, Jennifer M. Boggs, et al.
Implementation Research and Practice (2021) Vol. 2
Open Access | Times Cited: 18
The Veterans Health Administration REACH VET Program: Suicide Predictive Modeling in Practice
Bridget B. Matarazzo, Aaron Eagan, Sara J. Landes, et al.
Psychiatric Services (2022) Vol. 74, Iss. 2, pp. 206-209
Closed Access | Times Cited: 14
Implementing Machine Learning Models for Suicide Risk Prediction in Clinical Practice: Focus Group Study With Hospital Providers
Kate H. Bentley, Kelly L. Zuromski, Rebecca G. Fortgang, et al.
JMIR Formative Research (2022) Vol. 6, Iss. 3, pp. e30946-e30946
Open Access | Times Cited: 13
Intentional Self-Harm Among US Veterans With Traumatic Brain Injury or Posttraumatic Stress Disorder: Retrospective Cohort Study From 2008 to 2017
Bhanu Pratap Singh Rawat, Joel I. Reisman, Terri K. Pogoda, et al.
JMIR Public Health and Surveillance (2023) Vol. 9, pp. e42803-e42803
Open Access | Times Cited: 7
Highlighting psychological pain avoidance and decision‐making bias as key predictors of suicide attempt in major depressive disorder—A novel investigative approach using machine learning
Xinlei Ji, Jiahui Zhao, Lejia Fan, et al.
Journal of Clinical Psychology (2021) Vol. 78, Iss. 4, pp. 671-691
Closed Access | Times Cited: 16
AI-Based Prediction and Prevention of Psychological and Behavioral Changes in Ex-COVID-19 Patients
Krešimir Ćosić, Siniša Popović, Marko Šarlija, et al.
Frontiers in Psychology (2021) Vol. 12
Open Access | Times Cited: 16
Clinical implementation of suicide risk prediction models in healthcare: a qualitative study
Bobbi Jo H. Yarborough, Scott P. Stumbo, Jennifer L. Schneider, et al.
BMC Psychiatry (2022) Vol. 22, Iss. 1
Open Access | Times Cited: 12
Predicting inmate suicidal behavior with an interpretable ensemble machine learning approach in smart prisons
Khayyam Akhtar, Muhammad Usman Yaseen, Muhammad Imran, et al.
PeerJ Computer Science (2024) Vol. 10, pp. e2051-e2051
Open Access | Times Cited: 2
Using natural language processing to evaluate temporal patterns in suicide risk variation among high-risk Veterans
Maxwell Levis, Joshua Levy, Monica Dimambro, et al.
Psychiatry Research (2024) Vol. 339, pp. 116097-116097
Open Access | Times Cited: 2
Using Natural Language Processing to develop risk-tier specific suicide prediction models for Veterans Affairs patients
Maxwell Levis, Monica Dimambro, Joshua Levy, et al.
Journal of Psychiatric Research (2024) Vol. 179, pp. 322-329
Closed Access | Times Cited: 2
Deep learning for identifying personal and family history of suicidal thoughts and behaviors from EHRs
Prakash Adekkanattu, Al’ona Furmanchuk, Yonghui Wu, et al.
npj Digital Medicine (2024) Vol. 7, Iss. 1
Open Access | Times Cited: 2
Patient Feedback on the Use of Predictive Analytics for Suicide Prevention
Mark A. Reger, Brooke A. Ammerman, Sarah P. Carter, et al.
Psychiatric Services (2020) Vol. 72, Iss. 2, pp. 129-135
Closed Access | Times Cited: 18
Machine Learning Analysis of Handgun Transactions to Predict Firearm Suicide Risk
Hannah S. Laqueur, Colette Smirniotis, Christopher D. McCort, et al.
JAMA Network Open (2022) Vol. 5, Iss. 7, pp. e2221041-e2221041
Open Access | Times Cited: 10
Leveraging Natural Language Processing to Improve Electronic Health Record Suicide Risk Prediction for Veterans Health Administration Users
Maxwell Levis, Joshua Levy, Kallisse R. Dent, et al.
The Journal of Clinical Psychiatry (2023) Vol. 84, Iss. 4
Open Access | Times Cited: 6
Evaluation of Prevention Efforts and Risk Factors Among Veteran Suicide Decedents Who Died by Firearm
Brooke A. Ammerman, Mark A. Reger
Suicide and Life-Threatening Behavior (2020) Vol. 50, Iss. 3, pp. 679-687
Closed Access | Times Cited: 15
Sex‐Specific Risk Profiles for Suicide Among Persons with Substance Use Disorders in Denmark
Rachel Sayko Adams, Tammy Jiang, Anthony J. Rosellini, et al.
Addiction (2021) Vol. 116, Iss. 10, pp. 2882-2892
Open Access | Times Cited: 13
Suicide among Scottish military veterans: follow-up and trends
Beverly P Bergman, Daniel Mackay, Jill P. Pell
Occupational and Environmental Medicine (2021) Vol. 79, Iss. 2, pp. 88-93
Open Access | Times Cited: 13
Characteristics of suicide attempts associated with lethality and method: A latent class analysis of the Military Suicide Research Consortium
Molly Gromatsky, Emily Edwards, Sarah R. Sullivan, et al.
Journal of Psychiatric Research (2022) Vol. 149, pp. 54-61
Open Access | Times Cited: 9
Identifying suicide documentation in clinical notes through zero‐shot learning
T. Elizabeth Workman, Joseph L. Goulet, Cynthia Brandt, et al.
Health Science Reports (2023) Vol. 6, Iss. 9
Open Access | Times Cited: 5
Relative accuracy of social and medical determinants of suicide in electronic health records
Farrokh Alemi, Sanja Avramovic, Keith D. Renshaw, et al.
Health Services Research (2020) Vol. 55, Iss. S2, pp. 833-840
Open Access | Times Cited: 14
Suicide and the agent–host–environment triad: leveraging surveillance sources to inform prevention
Katherine M. Keyes, Sasikiran Kandula, Mark Olfson, et al.
Psychological Medicine (2021) Vol. 51, Iss. 4, pp. 529-537
Open Access | Times Cited: 12
From everyday life predictions to suicide prevention: Clinical and ethical considerations in suicide predictive analytic tools
Jeremy W. Luk, Larry D. Pruitt, Derek J. Smolenski, et al.
Journal of Clinical Psychology (2021) Vol. 78, Iss. 2, pp. 137-148
Closed Access | Times Cited: 12

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