This paper is in the following e-collection/theme issue:

Clinical Informatics (1120) Artificial Intelligence (1634) Clinical Information and Decision Making (1578) Machine Learning (1658) Diabetes (216) Artificial Intelligence in Diabetes Care and Prevention (35) Diabetic Retinopathy (12)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/41065, first published .
Development and External Validation of Machine Learning Models for Diabetic Microvascular Complications: Cross-Sectional Study With Metabolites

Development and External Validation of Machine Learning Models for Diabetic Microvascular Complications: Cross-Sectional Study With Metabolites

Development and External Validation of Machine Learning Models for Diabetic Microvascular Complications: Cross-Sectional Study With Metabolites

Authors of this article:

Feng He1, 2 Author Orcid Image ;   Clarissa Ng Yin Ling1 Author Orcid Image ;   Simon Nusinovici1, 3 Author Orcid Image ;   Ching-Yu Cheng1, 3 Author Orcid Image ;   Tien Yin Wong1, 3 Author Orcid Image ;   Jialiang Li2 Author Orcid Image ;   Charumathi Sabanayagam1, 3 Author Orcid Image
Article Authors Cited by (8) Tweetations (5) Metrics

Journals

  1. Reshetnikov A, Shaikhattarova N, Mazurok M, Kasatkina N. Dental Tissue Density in Healthy Children Based on Radiological Data: Retrospective Analysis. JMIRx Med 2024;5:e56759 View
  2. Wang L, Su J, Liu Z, Ding S, Li Y, Hou B, Hu Y, Dong Z, Tang J, Liu H, Liu W. Identification of immune-associated biomarkers of diabetes nephropathy tubulointerstitial injury based on machine learning: a bioinformatics multi-chip integrated analysis. BioData Mining 2024;17(1) View
  3. Vujosevic S, Limoli C, Nucci P. Novel artificial intelligence for diabetic retinopathy and diabetic macular edema: what is new in 2024?. Current Opinion in Ophthalmology 2024;35(6):472 View
  4. Song S, Yu J. Integrating Network Pharmacology, Bioinformatics, and Mendelian Randomization Analysis to Identify Hub Targets and Mechanisms of Kunkui Baoshen Decoction in Treating Diabetic Kidney Disease. Current Pharmaceutical Design 2024;30(42):3367 View
  5. Yang Y, Fan C, Zhang Y, Kang T, Jiang J. Untargeted Metabolomics Reveals the Role of Lipocalin-2 in the Pathological Changes of Lens and Retina in Diabetic Mice. Investigative Ophthalmology & Visual Science 2024;65(14):19 View
  6. Qin C, Peng L, Liu Y, Zhang X, Miao S, Wei Z, Feng W, Zhang H, Wan C, Yu Y, Lu S, Huang R, Zhang X. Development and Validation of a Nomogram-Based Model to Predict Primary Hypertension Within the Next Year in Children and Adolescents: Retrospective Cohort Study. Journal of Medical Internet Research 2024;26:e58686 View
  7. Zhang R, Chang R, Wang H, Chen J, Lu C, Fan K, Zhang Y, Li L, Yan S, Dong H. Untargeted metabolomic and proteomic analysis implicates SIRT2 as a novel therapeutic target for diabetic nephropathy. Scientific Reports 2025;15(1) View
  8. Li Y, Jin N, Zhan Q, Huang Y, Sun A, Yin F, Li Z, Hu J, Liu Z. Machine learning-based risk predictive models for diabetic kidney disease in type 2 diabetes mellitus patients: a systematic review and meta-analysis. Frontiers in Endocrinology 2025;16 View