cirpublications.com-49dy-1790880674-e370184423 1790880674 Clinical Intelligence Research Press info@cirpublications.com Clinical Intelligence Research Press Journal of Artificial Intelligence for Healthcare Systems J. Artif. Intell. Healthc. Syst. 3149-8981 2023 2 2 Carlos Ramirez Elena Torres Pablo Ortega Sofia Mendes 2023 10.68159/e370184423 https://cirpublications.com/pub/journal/1/article/e370184423 Ferguson T, Ravani P, Sood MM, Clarke A, Komenda P, Rigatto C, et al. Development and external validation of a machine learning model for progression of CKD. Kidney Int Rep. 2022;7(8):1772-81. 10.1016/j.ekir.2022.05.018 Zhong J, Yang HC, Fogo AB. A perspective on chronic kidney disease progression. Am J Physiol Renal Physiol. 2017;312(3):F375-84. 10.1152/ajprenal.00266.2016 Zee J, Mansfield S, Mariani LH, Gillespie BW. Using all longitudinal data to define time to specified percentages of estimated GFR decline: a simulation study. Am J Kidney Dis. 2019;73(1):82-9. 10.1053/j.ajkd.2018.07.019 Tsai MH, Jhou MJ, Liu TC, Fang YW, Lu CJ. An integrated machine learning predictive scheme for longitudinal laboratory data to evaluate the factors determining renal function changes in patients with different chronic kidney disease stages. Front Med (Lausanne). 2023;10:1155426. 10.3389/fmed.2023.1155426 Khan A, Uddin S, Srinivasan U. Comorbidity network for chronic disease: a novel approach to understand type 2 diabetes progression. Int J Med Inform. 2018;115:1-9. 10.1016/j.ijmedinf.2018.04.001 Su CT, Chang YP, Ku YT, Lin CM. Machine learning models for the prediction of renal failure in chronic kidney disease: a retrospective cohort study. Diagnostics (Basel). 2022;12(10):2454. 10.3390/diagnostics12102454 Aoki J, Kaya C, Khalid O, Kothari T, Silberman MA, Skordis C, et al. CKD progression prediction in a diverse US population: a machine-learning model. Kidney Med. 2023;5(9):100692. 10.1016/j.xkme.2023.100692 Zhang S, Tong H, Xu J, Maciejewski R. Graph convolutional networks: a comprehensive review. Comput Soc Netw. 2019;6(1):11. 10.1186/s40649-019-0069-y Kazi A, Farghadani S, Aganj I, Navab N. IA-GCN: interpretable attention based graph convolutional network for disease prediction. In: International Workshop on Machine Learning in Medical Imaging; 2023 Oct 8; Vancouver, Canada. Cham: Springer Nature Switzerland; 2023. p. 382-92. 10.1007/978-3-031-44521-7_38 Xing X, Yang F, Li H, Zhang J, Zhao Y, Gao M, et al. Multi-level attention graph neural network based on co-expression gene modules for disease diagnosis and prognosis. Bioinformatics. 2022;38(8):2178-86. Inaguma D, Hayashi H, Yanagiya R, Koseki A, Iwamori T, Kudo M, et al. Development of a machine learning-based prediction model for extremely rapid decline in estimated glomerular filtration rate in patients with chronic kidney disease: a retrospective cohort study using a large data set from a hospital in Japan. BMJ Open. 2022;12(6):e058833. 10.1136/bmjopen-2021-058833 Gregorich M, Kammer M, Heinzel A, Böger CA, Eckardt KU, Heerspink HJL, et al. Development and validation of a prediction model for future estimated glomerular filtration rate in people with type 2 diabetes and chronic kidney disease. JAMA Netw Open. 2023;6(4):e231870. 10.1001/jamanetworkopen.2023.1870 Zhu Y, Bi D, Saunders M, Ji Y. Prediction of chronic kidney disease progression using recurrent neural network and electronic health records. Sci Rep. 2023;13(1):22091. 10.1038/s41598-023-49252-1 Lee J, Warner E, Shaikhouni S, Bitzer M, Kretzler M, Gipson D, et al. Clustering-based spatial analysis framework through graph neural network for chronic kidney disease prediction using histopathology images. Sci Rep. 2023;13(1):12701. 10.1038/s41598-023-39854-0 Ghorbani M, Kazi A, Baghshah MS, Rabiee HR, Navab N. RA-GCN: graph convolutional network for disease prediction problems with imbalanced data. Med Image Anal. 2022;75:102272. 10.1016/j.media.2021.102272 Zhu W, Barreto EF, Li J, Lee HK, Kashani K. Drug-drug interaction and acute kidney injury development: a correlation-based network analysis. PLoS One. 2023;18(1):e0279928. 10.1371/journal.pone.0279928 Shin EK, Choi HY, Hayes N. The anatomy of COVID-19 comorbidity networks among hospitalized Korean patients. Epidemiol Health. 2021;43:e2021035. 10.4178/epih.e2021035 Dritsas E, Trigka M. Machine learning techniques for chronic kidney disease risk prediction. Big Data Cogn Comput. 2022;6(3):98. 10.3390/bdcc6030098 Kanda E, Epureanu BI, Adachi T, Kashihara N. Machine-learning-based web system for the prediction of chronic kidney disease progression and mortality. PLOS Digit Health. 2023;2(1):e0000188. 10.1371/journal.pdig.0000188 Kalantar-Zadeh K, Jafar TH, Nitsch D, Neuen BL, Perkovic V. Chronic kidney disease. Lancet. 2021;398(10302):786-802. 10.1016/S0140-6736(21)00519-5 Anders HJ, Huber TB, Isermann B, Schiffer M. CKD in diabetes: diabetic kidney disease versus nondiabetic kidney disease. Nat Rev Nephrol. 2018;14(6):361-77. 10.1038/s41581-018-0001-y Yuan Y, Bar-Joseph Z. GCNG: graph convolutional networks for inferring gene interaction from spatial transcriptomics data. Genome Biol. 2020;21(1):300. 10.1186/s13059-020-02214-3 Iftikhar H, Khan M, Khan Z, Khan F, Alshanbari HM, Ahmad Z. A comparative analysis of machine learning models: a case study in predicting chronic kidney disease. Sustainability. 2023;15(3):2754. 10.3390/su15032754