Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Explainable Machine Learning-Based Prediction Model for Diabetic Nephropathy
Jing-Mei Yin, Yang Li, Jun-Tang Xue, et al.
Journal of Diabetes Research (2024) Vol. 2024, pp. 1-13
Open Access | Times Cited: 10

Showing 10 citing articles:

Artificial intelligence applied to diabetes complications: a bibliometric analysis
Yu-Han Tao, Jinzheng Hou, Guangxin Zhou, et al.
Frontiers in Artificial Intelligence (2025) Vol. 8
Open Access | Times Cited: 1
Development and validation of predictive models for diabetic retinopathy using machine learning
Peigang Yang, Bin Yang
PLoS ONE (2025) Vol. 20, Iss. 2, pp. e0318226-e0318226
Open Access | Times Cited: 1
Machine learning-based risk predictive models for diabetic kidney disease in type 2 diabetes mellitus patients: a systematic review and meta-analysis
Yihan Li, Nan Jin, Qimin Zhan, et al.
Frontiers in Endocrinology (2025) Vol. 16
Open Access
An interpreting machine learning models to predict amputation risk in patients with diabetic foot ulcers: a multi-center study
Haoran Tao, Lili You, Yuhan Huang, et al.
Frontiers in Endocrinology (2025) Vol. 16
Open Access
Unveiling the utility of artificial intelligence for prediction, diagnosis, and progression of diabetic kidney disease: an evidence-based systematic review and meta-analysis
Sagar Dholariya, Siddhartha Dutta, Amit Sonagra, et al.
Current Medical Research and Opinion (2024), pp. 1-31
Closed Access | Times Cited: 1
Integrated machine learning and deep learning for predicting diabetic nephropathy model construction, validation, and interpretability
Junjie Ma, Shaoguang An, Mohan Cao, et al.
Endocrine (2024) Vol. 85, Iss. 2, pp. 615-625
Closed Access
Factors associated with acquiring exercise habits through health guidance for metabolic syndrome among middle-aged Japanese workers: A machine learning approach
Jiawei Wan, Kyohsuke Wakaba, Takeshi Onoue, et al.
Preventive Medicine Reports (2024) Vol. 48, pp. 102915-102915
Open Access
Development and validation of an interpretable machine learning model associated with erythrocyte fatty acids to identify coronary artery disease among Chinese adults
Yongjin Wang, Zhaocheng Zhuang, Yandan Wang, et al.
Food Bioscience (2024), pp. 105368-105368
Closed Access
Prevalence of Pre-diabetes, Diabetes and Associated Risk Factors among Staff and Students of Federal Polytechnic, Kaura-Namoda
Uthman Kabiru Samaila, Idris Aliyu Kankara
International Journal of Science for Global Sustainability (2023) Vol. 9, Iss. 4, pp. 90-94
Open Access
Page 1

Scroll to top