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

Artificial Intelligence (1409) Machine Learning (1491) Clinical Information and Decision Making (1426) Decision Support for Health Professionals (1203)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42181, first published .
Machine Learning for Predicting Micro- and Macrovascular Complications in Individuals With Prediabetes or Diabetes: Retrospective Cohort Study

Machine Learning for Predicting Micro- and Macrovascular Complications in Individuals With Prediabetes or Diabetes: Retrospective Cohort Study

Machine Learning for Predicting Micro- and Macrovascular Complications in Individuals With Prediabetes or Diabetes: Retrospective Cohort Study

Authors of this article:

Simon Schallmoser1, 2 Author Orcid Image ;   Thomas Zueger3, 4 Author Orcid Image ;   Mathias Kraus5 Author Orcid Image ;   Maytal Saar-Tsechansky6 Author Orcid Image ;   Christoph Stettler3 Author Orcid Image ;   Stefan Feuerriegel1, 2 Author Orcid Image
Article Authors Cited by (14) Tweetations (8) Metrics

Journals

  1. Kraus M, Feuerriegel S, Saar-Tsechansky M. Data-Driven Allocation of Preventive Care with Application to Diabetes Mellitus Type II. Manufacturing & Service Operations Management 2024;26(1):137 View
  2. Mora T, Roche D, Rodríguez-Sánchez B. Predicting the onset of diabetes-related complications after a diabetes diagnosis with machine learning algorithms. Diabetes Research and Clinical Practice 2023;204:110910 View
  3. Liu L, Bi B, Cao L, Gui M, Ju F. Predictive model and risk analysis for peripheral vascular disease in type 2 diabetes mellitus patients using machine learning and shapley additive explanation. Frontiers in Endocrinology 2024;15 View
  4. Bakris G, Lin P, Xu C, Chen C, Ashton V, Singhal M. Prediction of cardiovascular and renal risk among patients with apparent treatment‐resistant hypertension in the United States using machine learning methods. The Journal of Clinical Hypertension 2024;26(5):500 View
  5. Niu S, Yin Q, Ma J, Song Y, Xu Y, Bai L, Pan W, Yang X. Enhancing healthcare decision support through explainable AI models for risk prediction. Decision Support Systems 2024;181:114228 View
  6. Zhao Y, Lai Q, Tang H, Luo R, He Z, Huang W, Wang L, Zhang Z, Lin S, Qin W, Xu F. Identifying the risk factors of ICU-acquired fungal infections: clinical evidence from using machine learning. Frontiers in Medicine 2024;11 View
  7. Bantounou M, Nahar T, Plascevic J, Kumar N, Nath M, Myint P, Philip S. Drug Exposure As a Predictor in Diabetic Retinopathy Risk Prediction Models—A Systematic Review and Meta-Analysis. American Journal of Ophthalmology 2024;268:29 View
  8. Wu Y, Dong D, Zhu L, Luo Z, Liu Y, Xie X. Interpretable machine learning models for detecting peripheral neuropathy and lower extremity arterial disease in diabetics: an analysis of critical shared and unique risk factors. BMC Medical Informatics and Decision Making 2024;24(1) View
  9. Tang M, Zhao Y, Xiao J, Jiang S, Tan J, Xu Q, Pan C, Wang J. Development and validation of a predictive model for prolonged length of stay in elderly type 2 diabetes mellitus patients combined with cerebral infarction. Frontiers in Neurology 2024;15 View
  10. Wang X, Rao R, Li H, Lei X, Dong W. Red Blood Cell Transfusion for Incidence of Retinopathy of Prematurity: Prospective Multicenter Cohort Study. JMIR Pediatrics and Parenting 2024;7:e60330 View
  11. Caterson J, Lewin A, Williamson E. The application of explainable artificial intelligence (XAI) in electronic health record research: A scoping review. DIGITAL HEALTH 2024;10 View
  12. Zhang X, Yao W, Wang D, Hu W, Zhang G, Zhang Y. Development and Validation of Machine Learning Models for Identifying Prediabetes and Diabetes in Normoglycemia. Diabetes/Metabolism Research and Reviews 2024;40(8) View

Books/Policy Documents

  1. Medina-Pérez V, Zúñiga-Mondragón I, Cruz-Ramos J, Arellano-Arteaga K, Rusanova I, García-Gil G, López-Armas G. XLVI Mexican Conference on Biomedical Engineering. View
  2. Spallone V. Chronic Complications of Diabetes Mellitus. View