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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:

Designing risk prediction models for ambulatory no-shows across different specialties and clinics
Xiruo Ding, Ziad F. Gellad, Chad Mather, et al.
Journal of the American Medical Informatics Association (2018) Vol. 25, Iss. 8, pp. 924-930
Open Access | Times Cited: 52

Showing 1-25 of 52 citing articles:

Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU
Guilan Kong, Ke Lin, Yonghua Hu
BMC Medical Informatics and Decision Making (2020) Vol. 20, Iss. 1
Open Access | Times Cited: 111
Predictive Optimization of Patient No-Show Management in Primary Healthcare Using Machine Learning
Andrés Leiva-Araos, C. Contreras, Hemani Kaushal, et al.
Journal of Medical Systems (2025) Vol. 49, Iss. 1
Closed Access | Times Cited: 1
Patient No-Show Prediction: A Systematic Literature Review
Danae Carreras-García, David Delgado‐Gómez, Fernando Llorente, et al.
Entropy (2020) Vol. 22, Iss. 6, pp. 675-675
Open Access | Times Cited: 58
New feature selection methods based on opposition-based learning and self-adaptive cohort intelligence for predicting patient no-shows
Mohammed Aladeemy, Linda Adwan, Amy E. Booth, et al.
Applied Soft Computing (2019) Vol. 86, pp. 105866-105866
Closed Access | Times Cited: 56
Associations between Socioeconomic Factors and Visit Adherence among Patients with Glaucoma in the All of Us Research Program
Kaela Acuff, Arash Delavar, Bharanidharan Radha Saseendrakumar, et al.
Ophthalmology Glaucoma (2023) Vol. 6, Iss. 4, pp. 405-412
Open Access | Times Cited: 21
It’s how you say it: Systematic A/B testing of digital messaging cut hospital no-show rates
Adi Berliner Senderey, Tamar Kornitzer, Gabriella Lawrence, et al.
PLoS ONE (2020) Vol. 15, Iss. 6, pp. e0234817-e0234817
Open Access | Times Cited: 41
Machine learning-based prediction models for patients no-show in online outpatient appointments
Guorui Fan, Zhaohua Deng, Qing Ye, et al.
Data Science and Management (2021) Vol. 2, pp. 45-52
Open Access | Times Cited: 35
Decision analysis framework for predicting no-shows to appointments using machine learning algorithms
Carolina Deina, Flávio Sanson Fogliatto, Giovani J.C. da Silveira, et al.
BMC Health Services Research (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 5
Improving healthcare access management by predicting patient no-show behaviour
David Barrera, Sally Brailsford, Cristián Bravo, et al.
Decision Support Systems (2020) Vol. 138, pp. 113398-113398
Open Access | Times Cited: 38
Evaluating the Relationship between Neighborhood-Level Social Vulnerability and Patient Adherence to Ophthalmology Appointments
Angelica C. Scanzera, Sasha Kravets, Joelle Hallak, et al.
Ophthalmic Epidemiology (2023) Vol. 31, Iss. 1, pp. 11-20
Open Access | Times Cited: 12
Predicting patient no-shows using machine learning: A comprehensive review and future research agenda
Khaled M. Toffaha, Mecit Can Emre Simsekler, Mohammed Omar, et al.
Intelligence-Based Medicine (2025) Vol. 11, pp. 100229-100229
Open Access
Development, Implementation, and Evaluation of an In-Hospital Optimized Early Warning Score for Patient Deterioration
Cara O’Brien, Benjamin A. Goldstein, Yueqi Shen, et al.
MDM Policy & Practice (2020) Vol. 5, Iss. 1
Open Access | Times Cited: 32
Untapped potential of multicenter studies: a review of cardiovascular risk prediction models revealed inappropriate analyses and wide variation in reporting
Laure Wynants, David M. Kent, D. Timmerman, et al.
Diagnostic and Prognostic Research (2019) Vol. 3, Iss. 1
Open Access | Times Cited: 31
A metaheuristic-based stacking model for predicting the risk of patient no-show and late cancellation for neurology appointments
Ehsan Ahmadi, Andrés García-Arce, Dale T. Masel, et al.
IISE Transactions on Healthcare Systems Engineering (2019) Vol. 9, Iss. 3, pp. 272-291
Open Access | Times Cited: 27
Prevalence and predictors of no-shows to physical therapy for musculoskeletal conditions
Nrupen A. Bhavsar, Shannon M. Doerfler, Anna Giczewska, et al.
PLoS ONE (2021) Vol. 16, Iss. 5, pp. e0251336-e0251336
Open Access | Times Cited: 23
Reducing non-attendance in outpatient appointments: predictive model development, validation, and clinical assessment
Damià Valero-Bover, Pedro González, Gerard Carot-Sans, et al.
BMC Health Services Research (2022) Vol. 22, Iss. 1
Open Access | Times Cited: 14
Reducing Disparities in No Show Rates Using Predictive Model-Driven Live Appointment Reminders for At-Risk Patients: a Randomized Controlled Quality Improvement Initiative
Yasir Tarabichi, Jessica Higginbotham, Nicholas M. Riley, et al.
Journal of General Internal Medicine (2023) Vol. 38, Iss. 13, pp. 2921-2927
Open Access | Times Cited: 7
How patients distinguish between clinical and administrative predictive models in health care
Paige Nong, Julia Adler‐Milstein, Jodyn Platt
The American Journal of Managed Care (2024) Vol. 30, Iss. 1, pp. 31-37
Open Access | Times Cited: 2
Public perspectives on the use of different data types for prediction in healthcare
Paige Nong, Julia Adler‐Milstein, Sharon L. R. Kardia, et al.
Journal of the American Medical Informatics Association (2024) Vol. 31, Iss. 4, pp. 893-900
Open Access | Times Cited: 2
Development and Performance of a Clinical Decision Support Tool to Inform Resource Utilization for Elective Operations
Benjamin A. Goldstein, Marcelo Cerullo, Vijay Krishnamoorthy, et al.
JAMA Network Open (2020) Vol. 3, Iss. 11, pp. e2023547-e2023547
Open Access | Times Cited: 18
Prediction of appointment no-shows using electronic health records
Qiaohui Lin, Brenda Betancourt, Benjamin A. Goldstein, et al.
Journal of Applied Statistics (2019) Vol. 47, Iss. 7, pp. 1220-1234
Open Access | Times Cited: 18
Application of Machine Learning to Predict Patient No-Shows in an Academic Pediatric Ophthalmology Clinic
Jimmy Chen, I. Goldstein, Wei Chun Lin, et al.
American Medical Informatics Association Annual Symposium (2020) Vol. 2020, pp. 293-302
Closed Access | Times Cited: 15
Social determinants of health and the prediction of missed breast imaging appointments
Shahabeddin Sotudian, Aaron Afran, Christina A. LeBedis, et al.
BMC Health Services Research (2022) Vol. 22, Iss. 1
Open Access | Times Cited: 8
Comparisons of some new and existing EWMA schemes for binomial count processes with applications to monitoring hospital No-Shows
Xixi Qin, Jiujun Zhang, Amitava Mukherjee, et al.
Quality Technology & Quantitative Management (2024), pp. 1-23
Closed Access | Times Cited: 1
A novel model to label delirium in an intensive care unit from clinician actions
Caitlin E. Coombes, Kevin R. Coombes, Naleef Fareed
BMC Medical Informatics and Decision Making (2021) Vol. 21, Iss. 1
Open Access | Times Cited: 10
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