Published on in Vol 24, No 3 (2022): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26634, first published .
Machine Learning Prediction Models for Gestational Diabetes Mellitus: Meta-analysis

Machine Learning Prediction Models for Gestational Diabetes Mellitus: Meta-analysis

Machine Learning Prediction Models for Gestational Diabetes Mellitus: Meta-analysis

Journals

  1. Huang J, Yeung A, Armstrong D, Battarbee A, Cuadros J, Espinoza J, Kleinberg S, Mathioudakis N, Swerdlow M, Klonoff D. Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes. Journal of Diabetes Science and Technology 2023;17(1):224 View
  2. Jader R, Aminifar S, Ejbali R. Predictive Model for Diagnosis of Gestational Diabetes in the Kurdistan Region by a Combination of Clustering and Classification Algorithms: An Ensemble Approach. Applied Computational Intelligence and Soft Computing 2022;2022:1 View
  3. Qin Y, Wu J, Xiao W, Wang K, Huang A, Liu B, Yu J, Li C, Yu F, Ren Z. Machine Learning Models for Data-Driven Prediction of Diabetes by Lifestyle Type. International Journal of Environmental Research and Public Health 2022;19(22):15027 View
  4. Prudencio C, Nunes S, Pinheiro F, Filho C, Antônio F, de Aquino Nava G, Rudge M, Barbosa A, Calderon I, Souza F, Berghmans B, de Bie R, Thabane L, Junginger B, Graeff C, Magalhães C, Costa R, Lima S, Kron-Rodrigues M, Felisbino S, Barbosa W, Campos F, Bossolan G, Corrente J, Nunes H, Abbade J, Rossignoli P, Pedroni C, Atallah A, Di Bella Z, Uchoa S, Hungaro M, Mareco E, Sakalem M, Martinho N, Hallur L, Reyes D, Alves F, Marcondes J, Quiroz S, Pascon T, Catinelli B, Reis F, Oliveira R, Barneze S, Enriquez E, Takano L, Carr A, Magyori A, Iamundo L, Carvalho C, Jacomin M, Avramidis R, Silva A, Orlandi M, Dangió T, Bassin H, Melo J, Takemoto M, Menezes M, Caldeirão T, Santos N, Lourenço I, de Sá Marostica J, Caruso I, Rasmussen L, Garcia G, Pascon C, Bussaneli D, Nogueira V, Rudge C, Piculo F, Prata G, Barbosa V. Relaxin-2 during pregnancy according to glycemia, continence status, and pelvic floor muscle function. International Urogynecology Journal 2022;33(11):3203 View
  5. Nichols E, Pathak H, Bgeginski R, Mottola M, Giroux I, Van Lieshout R, Mohsenzadeh Y, Duerden E, Ab Wahab M. Machine learning-based predictive modeling of resilience to stressors in pregnant women during COVID-19: A prospective cohort study. PLOS ONE 2022;17(8):e0272862 View
  6. Akano T, James C. An assessment of ensemble learning approaches and single-based machine learning algorithms for the characterization of undersaturated oil viscosity. Beni-Suef University Journal of Basic and Applied Sciences 2022;11(1) View
  7. Huang Q, Hu Y, Wang C, Huang J, Shen M, Ren L. Clinical First-Trimester Prediction Models for Gestational Diabetes Mellitus: A Systematic Review and Meta-Analysis. Biological Research For Nursing 2023;25(2):185 View
  8. Arain Z, Iliodromiti S, Slabaugh G, David A, Chowdhury T. Machine learning and disease prediction in obstetrics. Current Research in Physiology 2023;6:100099 View
  9. Gokhale S, Taylor D, Gill J, Hu Y, Zeps N, Lequertier V, Teede H, Enticott J. Hospital length of stay prediction for general surgery and total knee arthroplasty admissions: Systematic review and meta-analysis of published prediction models. DIGITAL HEALTH 2023;9:205520762311774 View
  10. Wang J, Yin M, Wen H. Prediction performance of the machine learning model in predicting mortality risk in patients with traumatic brain injuries: a systematic review and meta-analysis. BMC Medical Informatics and Decision Making 2023;23(1) View
  11. Cubillos G, Monckeberg M, Plaza A, Morgan M, Estevez P, Choolani M, Kemp M, Illanes S, Perez C. Development of machine learning models to predict gestational diabetes risk in the first half of pregnancy. BMC Pregnancy and Childbirth 2023;23(1) View
  12. Song T, Feng L, Xia Y, Pang M, Geng J, Zhang X, Wang Y. Safety and efficacy of brivaracetam in children epilepsy: a systematic review and meta-analysis. Frontiers in Neurology 2023;14 View
  13. Belsti Y, Moran L, Du L, Mousa A, De Silva K, Enticott J, Teede H. Comparison of machine learning and conventional logistic regression-based prediction models for gestational diabetes in an ethnically diverse population; the Monash GDM Machine learning model. International Journal of Medical Informatics 2023;179:105228 View
  14. Yang X, Han R, Song Y, Zhang J, Huang H, Zhang J, Wang Y, Gao L. The Mediating Role of Physical Activity Self‐Efficacy in Predicting Moderate‐Intensity Physical Activity in Pregnant People at High Risk for Gestational Diabetes. Journal of Midwifery & Women's Health 2023 View
  15. Wang X, He C, Wu N, Tian Y, An S, Chen W, Liu X, Zhang H, Xiong S, Liu Y, Li Q, Zhou Y, Shen X. Establishment and validation of a prediction model for gestational diabetes. Diabetes, Obesity and Metabolism 2024;26(2):663 View
  16. Chen L, Shao X, Yu P. Machine learning prediction models for diabetic kidney disease: systematic review and meta-analysis. Endocrine 2023 View
  17. Gou H, Song H, Tian Z, Liu Y. Prediction models for children/adolescents with obesity/overweight: A systematic review and meta-analysis. Preventive Medicine 2024;179:107823 View
  18. El-Sofany H, El-Seoud S, Karam O, Abd El-Latif Y, Taj-Eddin I, Costa G. A Proposed Technique Using Machine Learning for the Prediction of Diabetes Disease through a Mobile App. International Journal of Intelligent Systems 2024;2024:1 View
  19. Vivek Khanna V, Chadaga K, Sampathila N, Prabhu S, Chadaga P. R, Bhat D, K. S. S. Explainable artificial intelligence-driven gestational diabetes mellitus prediction using clinical and laboratory markers. Cogent Engineering 2024;11(1) View
  20. Nuthakki P, Kumar T. Machine learning-based early detection of diabetes risk factors for improved health management. Multimedia Tools and Applications 2024 View

Books/Policy Documents

  1. Medida L, Renugadevi R. Predicting Pregnancy Complications Through Artificial Intelligence and Machine Learning. View
  2. Priyanka , Goyal S, Bhatia R. Communication and Intelligent Systems. View