Published on in Vol 7, No 3 (2022): Jul-Sep

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/32366, first published .
Machine Learning–Derived Prenatal Predictive Risk Model to Guide Intervention and Prevent the Progression of Gestational Diabetes Mellitus to Type 2 Diabetes: Prediction Model Development Study

Machine Learning–Derived Prenatal Predictive Risk Model to Guide Intervention and Prevent the Progression of Gestational Diabetes Mellitus to Type 2 Diabetes: Prediction Model Development Study

Machine Learning–Derived Prenatal Predictive Risk Model to Guide Intervention and Prevent the Progression of Gestational Diabetes Mellitus to Type 2 Diabetes: Prediction Model Development Study

Journals

  1. Tang L, Xu Z, Yao P, Zhu H, Tang M. Meta-Analysis of the Effect of Nursing Intervention on Children with Type 2 Diabetes. Computational and Mathematical Methods in Medicine 2022;2022:1 View
  2. Song J, Gao J, Zhang Y, Li F, Man W, Liu M, Wang J, Li M, Zheng H, Yang X, Li C. Estimation of Soil Organic Carbon Content in Coastal Wetlands with Measured VIS-NIR Spectroscopy Using Optimized Support Vector Machines and Random Forests. Remote Sensing 2022;14(17):4372 View
  3. Morgan-Benita J, Sánchez-Reyna A, Espino-Salinas C, Oropeza-Valdez J, Luna-García H, Galván-Tejada C, Galván-Tejada J, Gamboa-Rosales H, Enciso-Moreno J, Celaya-Padilla J. Metabolomic Selection in the Progression of Type 2 Diabetes Mellitus: A Genetic Algorithm Approach. Diagnostics 2022;12(11):2803 View
  4. Tsai S, Yang C, Liu W, Lee C. Development and validation of an insulin resistance model for a population without diabetes mellitus and its clinical implication: a prospective cohort study. eClinicalMedicine 2023;58:101934 View
  5. Zou C, Zhang Y, Yuan Z. An intelligent adverse delivery outcomes prediction model based on the fusion of multiple obstetric clinical data. Computer Methods in Biomechanics and Biomedical Engineering 2023:1 View
  6. Liu Z, Jia N, Zhang Q, Liu W. Risk prediction models for postpartum glucose intolerance in women with a history of gestational diabetes mellitus: a scoping review. Journal of Diabetes & Metabolic Disorders 2023 View
  7. Holt J, Talsma A, Johnson T, Ehlinger T. Artificial neural network approaches to identify maternal and infant risk and asset factors using Peridata.Net: a WI-MIOS study. JAMIA Open 2023;6(3) View
  8. Parkhi D, Periyathambi N, Ghebremichael-Weldeselassie Y, Patel V, Sukumar N, Siddharthan R, Narlikar L, Saravanan P. Prediction of postpartum prediabetes by machine learning methods in women with gestational diabetes mellitus. iScience 2023;26(10):107846 View
  9. Hanna F, Wu P, Heald A, Fryer A. Diabetes detection in women with gestational diabetes and polycystic ovarian syndrome. BMJ 2023:e071675 View
  10. Yang J, Wan J, Feng L, Hou S, Yv K, Xu L, Chen K. Machine learning algorithms for the prediction of adverse prognosis in patients undergoing peritoneal dialysis. BMC Medical Informatics and Decision Making 2024;24(1) View
  11. Lu H, Ding X, Hirst J, Yang Y, Yang J, Mackillop L, Clifton D. Digital Health and Machine Learning Technologies for Blood Glucose Monitoring and Management of Gestational Diabetes. IEEE Reviews in Biomedical Engineering 2024;17:98 View
  12. nimmagadda S, Suryanarayana G, Kumar G, Anudeep G, Sai G. A Comprehensive Survey on Diabetes Type-2 (T2D) Forecast Using Machine Learning. Archives of Computational Methods in Engineering 2024 View

Books/Policy Documents

  1. Fathima M, Singh P, Ammal M, Hariharan R. Advanced Network Technologies and Intelligent Computing. View