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

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