Published on in Vol 8, No 1 (2020): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/15510, first published .
Longitudinal Risk Prediction of Chronic Kidney Disease in Diabetic Patients Using a Temporal-Enhanced Gradient Boosting Machine: Retrospective Cohort Study

Longitudinal Risk Prediction of Chronic Kidney Disease in Diabetic Patients Using a Temporal-Enhanced Gradient Boosting Machine: Retrospective Cohort Study

Longitudinal Risk Prediction of Chronic Kidney Disease in Diabetic Patients Using a Temporal-Enhanced Gradient Boosting Machine: Retrospective Cohort Study

Journals

  1. Li F, Du J, He Y, Song H, Madkour M, Rao G, Xiang Y, Luo Y, Chen H, Liu S, Wang L, Liu H, Xu H, Tao C. Time event ontology (TEO): to support semantic representation and reasoning of complex temporal relations of clinical events. Journal of the American Medical Informatics Association 2020;27(7):1046 View
  2. Thongprayoon C, Kaewput W, Kovvuru K, Hansrivijit P, Kanduri S, Bathini T, Chewcharat A, Leeaphorn N, Gonzalez-Suarez M, Cheungpasitporn W. Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation. Journal of Clinical Medicine 2020;9(4):1107 View
  3. Ravaut M, Sadeghi H, Leung K, Volkovs M, Kornas K, Harish V, Watson T, Lewis G, Weisman A, Poutanen T, Rosella L. Predicting adverse outcomes due to diabetes complications with machine learning using administrative health data. npj Digital Medicine 2021;4(1) View
  4. Krishnamurthy S, KS K, Dovgan E, Luštrek M, Gradišek Piletič B, Srinivasan K, Li Y, Gradišek A, Syed-Abdul S. Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan. Healthcare 2021;9(5):546 View
  5. Zhou L, Zheng X, Yang D, Wang Y, Bai X, Ye X. Application of multi-label classification models for the diagnosis of diabetic complications. BMC Medical Informatics and Decision Making 2021;21(1) View
  6. Schallmoser S, Zueger T, Kraus M, Saar-Tsechansky M, Stettler C, Feuerriegel S. Machine Learning for Predicting Micro- and Macrovascular Complications in Individuals With Prediabetes or Diabetes: Retrospective Cohort Study. Journal of Medical Internet Research 2023;25:e42181 View
  7. Schena F, Anelli V, Abbrescia D, Di Noia T. Prediction of chronic kidney disease and its progression by artificial intelligence algorithms. Journal of Nephrology 2022;35(8):1953 View
  8. Tan K, Seng J, Kwan Y, Chen Y, Zainudin S, Loh D, Liu N, Low L. Evaluation of Machine Learning Methods Developed for Prediction of Diabetes Complications: A Systematic Review. Journal of Diabetes Science and Technology 2023;17(2):474 View
  9. Brady V, Whisenant M, Wang X, Ly V, Zhu G, Aguilar D, Wu H. Characterization of Symptoms and Symptom Clusters for Type 2 Diabetes Using a Large Nationwide Electronic Health Record Database. Diabetes Spectrum 2022;35(2):159 View
  10. Mosa A, Thongmotai C, Islam H, Paul T, Hossain K, Mandhadi V. Evaluation of machine learning applications using real-world EHR data for predicting diabetes-related long-term complications. Journal of Business Analytics 2022;5(2):141 View
  11. Singh V, Asari V, Rajasekaran R. A Deep Neural Network for Early Detection and Prediction of Chronic Kidney Disease. Diagnostics 2022;12(1):116 View
  12. Zhang X, Liu K, Yuan B, Wang H, Chen S, Xue Y, Chen W, Liu M, Hu Y. A hybrid adaptive approach for instance transfer learning with dynamic and imbalanced data. International Journal of Intelligent Systems 2022;37(12):11582 View
  13. Kanda E, Suzuki A, Makino M, Tsubota H, Kanemata S, Shirakawa K, Yajima T. Machine learning models for prediction of HF and CKD development in early-stage type 2 diabetes patients. Scientific Reports 2022;12(1) View
  14. Momenzadeh A, Shamsa A, Meyer J. Bias or biology? Importance of model interpretation in machine learning studies from electronic health records. JAMIA Open 2022;5(3) View
  15. Cao X, Lin Y, Yang B, Li Y, Zhou J. Comparison Between Statistical Model and Machine Learning Methods for Predicting the Risk of Renal Function Decline Using Routine Clinical Data in Health Screening. Risk Management and Healthcare Policy 2022;Volume 15:817 View
  16. Wu C, Zhou T, Tian Y, Wu J, Li J, Liu Z. A method for the early prediction of chronic diseases based on short sequential medical data. Artificial Intelligence in Medicine 2022;127:102262 View
  17. Sanmarchi F, Fanconi C, Golinelli D, Gori D, Hernandez-Boussard T, Capodici A. Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review. Journal of Nephrology 2023;36(4):1101 View
  18. Gündoğdu S. Efficient prediction of early-stage diabetes using XGBoost classifier with random forest feature selection technique. Multimedia Tools and Applications 2023;82(22):34163 View
  19. Sabanayagam C, He F, Nusinovici S, Li J, Lim C, Tan G, Cheng C. Prediction of diabetic kidney disease risk using machine learning models: A population-based cohort study of Asian adults. eLife 2023;12 View
  20. Liu X, Duan M, Huang H, Zhang Y, Xiang T, Niu W, Zhou B, Wang H, Zhang T. Predicting diabetic kidney disease for type 2 diabetes mellitus by machine learning in the real world: a multicenter retrospective study. Frontiers in Endocrinology 2023;14 View
  21. Khade A, Vidhate A, Vidhate D. FFN-XGB- design of a hybrid feed forward neural network and extreme gradient boosting model for early prediction of chronic kidney disease. International Journal of System Assurance Engineering and Management 2023 View
  22. Mesquita F, Bernardino J, Henriques J, Raposo J, Ribeiro R, Paredes S. Machine learning techniques to predict the risk of developing diabetic nephropathy: a literature review. Journal of Diabetes & Metabolic Disorders 2023;23(1):825 View
  23. Pradeepa P, Jeyakumar D. Chronic kidney disease prediction using improved deep belief network with local search nearest neighbour optimization. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2024;11(7) View
  24. Rehman A, Xing H, Hussain M, Gulzar N, Khan M, Hussain A, Mahmood S. HCDP-DELM: Heterogeneous chronic disease prediction with temporal perspective enabled deep extreme learning machine. Knowledge-Based Systems 2024;284:111316 View
  25. Fu Z, Wang Z, Clemente K, Jaisinghani M, Poon K, Yeo A, Ang G, Liew A, Lim C, Foo M, Chow W, Ta W. Development and deployment of a nationwide predictive model for chronic kidney disease progression in diabetic patients. Frontiers in Nephrology 2024;3 View
  26. Sheng Y, Zhang C, Huang J, Wang D, Xiao Q, Zhang H, Ha X. Comparison of conventional mathematical model and machine learning model based on recent advances in mathematical models for predicting diabetic kidney disease. DIGITAL HEALTH 2024;10 View
  27. Pingi S, Nayak R, Bashar M. Conditional Generative Adversarial Network for Early Classification of Longitudinal Datasets Using an Imputation Approach. ACM Transactions on Knowledge Discovery from Data 2024;18(5):1 View
  28. Kumar M. Early detection of chronic kidney disease using recursive feature elimination and cross-validated XGBoost model. International Journal of Computational Materials Science and Engineering 2024;13(04) View
  29. Shi L, Xue Y, Yu X, Wang Y, Hong T, Li X, Ma J, Zhu D, Mu Y. Prevalence and Risk Factors of Chronic Kidney Disease in Patients With Type 2 Diabetes in China: Cross-Sectional Study. JMIR Public Health and Surveillance 2024;10:e54429 View
  30. Dholariya S, Dutta S, Sonagra A, Kaliya M, Singh R, Parchwani D, Motiani A. Unveiling the utility of artificial intelligence for prediction, diagnosis, and progression of diabetic kidney disease: an evidence-based systematic review and meta-analysis. Current Medical Research and Opinion 2024:1 View

Books/Policy Documents

  1. Hong N, Park Y, You S, Rhee Y. Artificial Intelligence in Medicine. View
  2. Hong N, Park Y, You S, Rhee Y. Artificial Intelligence in Medicine. View
  3. Sharma V, Sharma P. Advances in Data and Information Sciences. View
  4. Chapa K, Ravi B. Soft Computing and Signal Processing. View
  5. Lakshmi Prasudha M, Vidyullatha S, Divya Y. Advances in Data-Driven Computing and Intelligent Systems. View
  6. Martínez Marquina L, Núñez Anglada N, Varona Arche J, Mora Jiménez I. Bioinformatics and Biomedical Engineering. View