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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 Treatment Outcomes Using Explainable Machine Learning in Children with Asthma
Mario Lovrić, Ivana Banić, Emanuel Lacić, et al.
Children (2021) Vol. 8, Iss. 5, pp. 376-376
Open Access | Times Cited: 19

Showing 19 citing articles:

Prediction of Type 2 Diabetes Based on Machine Learning Algorithm
Henock M. Deberneh, Intaek Kim
International Journal of Environmental Research and Public Health (2021) Vol. 18, Iss. 6, pp. 3317-3317
Open Access | Times Cited: 148
Current state and prospects of artificial intelligence in allergy
Merlijn van Breugel, Rudolf S.N. Fehrmann, Marnix Bügel, et al.
Allergy (2023) Vol. 78, Iss. 10, pp. 2623-2643
Open Access | Times Cited: 15
Epigenomic partitioning of a polygenic risk score for asthma reveals distinct genetically driven disease pathways
Bernard Stikker, Lianne Trap, Bahareh Sedaghati-khayat, et al.
European Respiratory Journal (2024) Vol. 64, Iss. 2, pp. 2302059-2302059
Open Access | Times Cited: 5
Application of artificial intelligence in pediatric wheezing illnesses
Zhoumeng Ying, Lan Song, Zhengyu Jin
Chinese Journal of Academic Radiology (2025)
Closed Access
Prediction Models of Early Childhood Caries Based on Machine Learning Algorithms
Yu Hyun Park, Sung Hwa Kim, Yoon Young Choi
International Journal of Environmental Research and Public Health (2021) Vol. 18, Iss. 16, pp. 8613-8613
Open Access | Times Cited: 38
Machine learning classification approach for asthma prediction models in children
Raphael Henshaw Ekpo, Victor Chukwudi Osamor, Ambrose Azeta, et al.
Health and Technology (2023) Vol. 13, Iss. 1, pp. 1-10
Closed Access | Times Cited: 12
Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review
Widana Kankanamge Darsha Jayamini, Farhaan Mirza, M. Asif Naeem, et al.
Journal of Medical Systems (2024) Vol. 48, Iss. 1
Open Access | Times Cited: 3
Implementation of prognostic machine learning algorithms in paediatric chronic respiratory conditions: a scoping review
Nicole Filipow, Eleanor Main, Neil J. Sebire, et al.
BMJ Open Respiratory Research (2022) Vol. 9, Iss. 1, pp. e001165-e001165
Open Access | Times Cited: 13
Predictive Capability of QSAR Models Based on the CompTox Zebrafish Embryo Assays: An Imbalanced Classification Problem
Mario Lovrić, Olga Malev, Göran Klobučar, et al.
Molecules (2021) Vol. 26, Iss. 6, pp. 1617-1617
Open Access | Times Cited: 14
Characteristics of Severe Asthma Patients and Predictors of Asthma Control in the Swiss Severe Asthma Registry
Fabienne Jaun, Lydia Marie Tröster, Stéphanie Giezendanne, et al.
Respiration (2023) Vol. 102, Iss. 10, pp. 863-878
Open Access | Times Cited: 5
Machine learning model for classification of predominantly allergic and non-allergic asthma among preschool children with asthma hospitalization
Piyush Bhardwaj, Ashish Tyagi, Shashank Tyagi, et al.
Journal of Asthma (2022) Vol. 60, Iss. 3, pp. 487-495
Closed Access | Times Cited: 9
State of Asthma-Related Hospital Admissions in New Zealand and Predicting Length of Stay Using Machine Learning
Widana Kankanamge Darsha Jayamini, Farhaan Mirza, M. Asif Naeem, et al.
Applied Sciences (2022) Vol. 12, Iss. 19, pp. 9890-9890
Open Access | Times Cited: 5
Treatment outcome clustering patterns correspond to discrete asthma phenotypes in children
Ivana Banić, Mario Lovrić, Gerald Cuder, et al.
Asthma Research and Practice (2021) Vol. 7, Iss. 1
Open Access | Times Cited: 6
Deployment of a Phenotypic Characterization System for Effective Identification of the Onset of Asthma Disease
M R Pooja, Vinayakumar Ravi, Alanoud Al Mazroa, et al.
The Open Public Health Journal (2024) Vol. 17, Iss. 1
Open Access
Machine Learning for the Identification of Key Predictors to Bayley Outcomes: A Preterm Cohort Study
Petra Grđan Stevanović, Nina Barišić, Iva Šunić, et al.
Journal of Personalized Medicine (2024) Vol. 14, Iss. 9, pp. 922-922
Open Access
An IoT-Based Asthma Intensity Prediction Using Classification Models
J.N.V.R. Swarup Kumar, Badal Nishant, S. Venkata Suraj, et al.
(2024), pp. 404-410
Closed Access
Artificial intelligence in pediatric allergy research
Daniil Lisik, Rani Basna, Duy-Tai Dinh, et al.
European Journal of Pediatrics (2024) Vol. 184, Iss. 1
Open Access
Persistent Asthma in Childhood
Cilla Söderhäll, Ann‐Marie Malby Schoos
Children (2022) Vol. 9, Iss. 6, pp. 820-820
Open Access | Times Cited: 2
Diabetes prediction using machine learning tools
Jeevan Kumar, Rajesh Tiwari, Vijay Pandey
(2022), pp. 263-267
Closed Access | Times Cited: 2
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