Predicting the early risk of chronic kidney disease in patients with diabetes using real-world data
Abstract
Diagnostic procedures, therapeutic recommendations, and medical risk stratifications are based on dedicated, strictly controlled clinical trials. However, a plethora of real-world medical data exists, whereupon the increase in data volume comes at the expense of completeness, uniformity, and control. Here, a case-by-case comparison shows that the predictive power of our real world data-based model for diabetes-related chronic kidney disease outperforms published algorithms, which were derived from clinical study data.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Ravizza et al. employed a data-driven feature selection strategy to identify seven key predictors, including age, BMI, eGFR, creatinine, glucose, albumin, and HbA1c, for CKD prediction with large-scale EHR data.
“Ravizza et al. [74] employed a data-driven feature selection strategy to identify seven key predictors, including age, BMI, eGFR, creatinine, glucose, albumin, and HbA1c, for chronic kidney disease (CKD) prediction with large-scale EHR data.”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [74]
A partnership among Roche Diabetes Care GmbH, IBM, Eli Lilly and Company, the Regenstrief Institute, and the Indiana Biosciences Research Institute developed a predictive algorithm identifying patients at risk for chronic kidney disease.
“For example, a partnership among Roche Diabetes Care GmbH, IBM, Eli Lilly and Company, the Regenstrief Institute, and the Indiana Biosciences Research Institute developed a predictive algorithm identifying patients at risk for chronic kidney disease [43].”
From Evolution of clinical Health Information Exchanges to population health resources: a case study of the Indiana network for patient care (2025) · cites this paper as [43]
Cites
(1993) · New England Journal of Medicine · doi:10.1056/nejm199309303291401 · Cited by 24,667 · see citing works
Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013) · Wiley Series in Probability and Statistics · doi:10.1002/9781118548387 · Cited by 13,861 · see citing works
pROC: an open-source package for R and S+ to analyze and compare ROC curves
Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., & Müller, M. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-77 · Cited by 13,697 · see citing works
Measuring the Accuracy of Diagnostic Systems
Swets, J. A. (1988) · Science · doi:10.1126/science.3287615 · Cited by 10,058 · see citing works
The use of the area under the ROC curve in the evaluation of machine learning algorithms
Bradley, A. P. (1997) · Pattern Recognition · doi:10.1016/s0031-3203(96)00142-2 · Cited by 7,217 · see citing works
The ASA Statement on <i>p</i> -Values: Context, Process, and Purpose
Wasserstein, R. L., & Lazar, N. A. (2016) · The American Statistician · doi:10.1080/00031305.2016.1154108 · Cited by 6,306 · see citing works
Purposeful selection of variables in logistic regression
Bursac, Z., Gauss, C. H., Williams, D. K., & Hosmer, D. W. (2008) · Source Code for Biology and Medicine · doi:10.1186/1751-0473-3-17 · Cited by 4,035 · see citing works
Empagliflozin and Progression of Kidney Disease in Type 2 Diabetes
Wanner, C., Inzucchi, S. E., Lachin, J. M., Fitchett, D., von Eynatten, M., Mattheus, M., Johansen, O. E., Woerle, H. J., Broedl, U. C., & Zinman, B. (2016) · New England Journal of Medicine · doi:10.1056/nejmoa1515920 · Cited by 3,655 · see citing works
Wu, X., Zhu, X., Wu, G.-Q., & Ding, W. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.109 · Cited by 2,410 · see citing works
Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T. (2016) · Scientific Reports · doi:10.1038/srep26094 · Cited by 1,710 · see citing works
Big Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients
Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A., & Escobar, G. (2014) · Health Affairs · doi:10.1377/hlthaff.2014.0041 · Cited by 1,235 · see citing works
The big challenges of big data
Marx, V. (2013) · Nature · doi:10.1038/498255a · Cited by 1,233 · see citing works
Global kidney health 2017 and beyond: a roadmap for closing gaps in care, research, and policy
Levin, A., Tonelli, M. A., Bonventre, J. V., Coresh, J., Donner, J., Fogo, A. B., Fox, C. S., Gansevoort, R. T., Heerspink, H. J. L., Jardine, M., et al. (2017) · The Lancet · doi:10.1016/s0140-6736(17)30788-2 · Cited by 948 · see citing works
Evidence for Health Decision Making — Beyond Randomized, Controlled Trials
Frieden, T. R. (2017) · New England Journal of Medicine · doi:10.1056/nejmra1614394 · Cited by 770 · see citing works
The global burden of chronic kidney disease: estimates, variability and pitfalls
Glassock, R. J., Warnock, D. G., & Delanaye, P. (2016) · Nature Reviews Nephrology · doi:10.1038/nrneph.2016.163 · Cited by 508 · see citing works
The Indiana Network For Patient Care: A Working Local Health Information Infrastructure
McDonald, C. J., Overhage, J. M., Barnes, M., Schadow, G., Blevins, L., Dexter, P. R., Mamlin, B., & the INPC Management Committee (2005) · Health Affairs · doi:10.1377/hlthaff.24.5.1214 · Cited by 330 · see citing works
Population-Level Prediction of Type 2 Diabetes From Claims Data and Analysis of Risk Factors
Razavian, N., Blecker, S., Schmidt, A. M., Smith–McLallen, A., Nigam, S., & Sontag, D. A. (2015) · Big Data · doi:10.1089/big.2015.0020 · Cited by 213 · see citing works
Awareness of Chronic Kidney Disease Among Patients and Providers
Plantinga, L., Tuot, D. S., & Powe, N. R. (2010) · Advances in Chronic Kidney Disease · doi:10.1053/j.ackd.2010.03.002 · Cited by 203 · see citing works
Risk Scores for Predicting Outcomes in Patients with Type 2 Diabetes and Nephropathy
Keane, W. F., Zhang, Z., Lyle, P. A., Cooper, M. E., de Zeeuw, D., Grünfeld, J., Lash, J. P., McGill, J. B., Mitch, W. E., Remuzzi, G., et al. (2006) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.01381005 · Cited by 201 · see citing works
Treatment decisions in multiple sclerosis — insights from real-world observational studies
Trojano, M., Tintoré, M., Montalbán, X., Hillert, J. A., Kalinčík, T., Iaffaldano, P., Spelman, T. D., Sormani, M. P., & Butzkueven, H. (2017) · Nature Reviews Neurology · doi:10.1038/nrneurol.2016.188 · Cited by 190 · see citing works
Bag Constrained Structure Pattern Mining for Multi-Graph Classification
Wu, J. X., Xingquan Zhu, A., Zhang, C., & Yu, P. S. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.2297923 · Cited by 168 · see citing works
Risk Prediction for Early CKD in Type 2 Diabetes
Dunkler, D., Gao, P., Lee, S. F., Heinze, G., Clase, C. M., Tobe, S. W., Teo, K. K., Gerstein, H. C., Mann, J. F. E., & Oberbauer, R. W. (2015) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.10321014 · Cited by 136 · see citing works
Prediction of Kidney-Related Outcomes in Patients With Type 2 Diabetes
Jardine, M., Hata, J., Woodward, M., Perkovic, V., Ninomiya, T., Arima, H., Zoungas, S., Cass, A., Patel, A. A., Marre, M., et al. (2012) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2012.04.025 · Cited by 134 · see citing works
Kaelber, D. C., Foster, W., Gilder, J. R., Love, T. E., & Jain, A. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000782 · Cited by 124 · see citing works
Residual microvascular risk in diabetes: unmet needs and future directions
Fioretto, P., Dodson, P. M., Ziegler, D., & Rosenson, R. S. (2009) · Nature Reviews Endocrinology · doi:10.1038/nrendo.2009.213 · Cited by 110 · see citing works
Voosen, P. (2017) · Science · doi:10.1126/science.357.6346.22 · Cited by 101 · see citing works
Progression to microalbuminuria in type 1 diabetes: development and validation of a prediction rule
Vergouwe, Y., Soedamah‐Muthu, S. S., Zgibor, J. C., Chaturvedi, N., Forsblom, C. M., Snell‐Bergeon, J. K., Maahs, D. M., Groop, P., Rewers, M., Orchard, T. J., et al. (2009) · Diabetologia · doi:10.1007/s00125-009-1585-3 · Cited by 69 · see citing works
Big data in biomedicine: 4 big questions
Bender, E. (2015) · Nature · doi:10.1038/527s19a · Cited by 19 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Zhang, K., Liu, X., Shen, J., Li, Z., Sang, Y., Wu, X., Zha, Y., Liang, W., Wang, C., Wang, K., et al. (2020) · Cell · doi:10.1016/j.cell.2020.04.045 · Cited by 917 · see citing works
Rahman, A., Hossain, M. S., Muhammad, G., Kundu, D., Debnath, T., Rahman, M., Khan, M. S. I., Tiwari, P., & Band, S. S. (2023) · Cluster Computing · doi:10.1007/s10586-022-03658-4 · Cited by 399 · see citing works
Deep learning approach for diabetes prediction using PIMA Indian dataset
Naz, H. U., & Ahuja, S. (2020) · Journal of Diabetes & Metabolic Disorders · doi:10.1007/s40200-020-00520-5 · Cited by 359 · see citing works
Zhang, K., Liu, X., Xu, J., Yuan, J., Cai, W., Chen, T., Wang, K., Gao, Y., Nie, S., Xu, X., et al. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-021-00745-6 · Cited by 324 · see citing works
Chronic kidney disease prediction based on machine learning algorithms
Islam, M. A., Majumder, M. Z. H., & Hussein, M. A. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2023.100189 · Cited by 313 · see citing works
Development of Risk Prediction Equations for Incident Chronic Kidney Disease
Nelson, R. G., Grams, M. E., Ballew, S. H., Sang, Y., Azizi, F., Chadban, S. J., Chaker, L., Dunning, S. C., Fox, C. S., Hirakawa, Y., et al. (2019) · JAMA · doi:10.1001/jama.2019.17379 · Cited by 249 · see citing works
Senan, E. M., Al-Adhaileh, M. H., Alsaade, F. W., Aldhyani, T. H. H., Alqarni, A. A., Alsharif, N. H., Uddin, M. I., Alahmadi, A. H., Jadhav, M. E., & Alzahrani, M. Y. (2021) · Journal of Healthcare Engineering · doi:10.1155/2021/1004767 · Cited by 248 · see citing works
Jiang, W., Wang, J., Shen, X., Lu, W., Wang, Y., Li, W., Gao, Z., Xu, J., Li, X., Liu, R., et al. (2020) · Diabetes Care · doi:10.2337/dc19-1897 · Cited by 234 · see citing works
Wang, G., Liu, X., Shen, J., Wang, C., Li, Z., Ye, L., Wu, X., Chen, T., Wang, K., Zhang, X., et al. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-021-00704-1 · Cited by 192 · see citing works
A Universal Artificial Intelligence Platform for Collaborative Management of Cataracts
Wu, X., Huang, Y., Liu, Z., Lai, W., Long, E., Zhang, K., Jiang, J., Lin, D., Chen, K., Wu, D., et al. (2019) · The Lancet · doi:10.1016/s0140-6736(19)32358-x · Cited by 159 · see citing works
Alfego, D., Ennis, J. L., Gillespie, B. S., Lewis, M. J., Montgomery, E., Ferrè, S., Vassalotti, J. A., & Letovsky, S. I. (2021) · Diabetes Care · doi:10.2337/dc21-0723 · Cited by 133 · see citing works
Song, X., Yu, A. S. L., Kellum, J. A., Waitman, L. R., Matheny, M. E., Simpson, S. Q., Hu, Y., & Liu, M. (2020) · Nature Communications · doi:10.1038/s41467-020-19551-w · Cited by 120 · see citing works
Wang, Q., Cao, W., Guo, J., Ren, J., Cheng, Y., & Davis, D. N. (2019) · IEEE Access · doi:10.1109/access.2019.2929866 · Cited by 119 · see citing works
Zou, Y., Zhao, L., Zhang, J., Wang, Y., Wu, Y., Ren, H., Wang, T., Zhang, R., Wang, J., Zhao, Y., et al. (2022) · Renal Failure · doi:10.1080/0886022x.2022.2056053 · Cited by 111 · see citing works
Dong, Z., Wang, Q., Ke, Y., Zhang, W., Hong, Q., Liu, C., Liu, X., Yang, J., Xi, Y., Shi, J., et al. (2022) · Journal of Translational Medicine · doi:10.1186/s12967-022-03339-1 · Cited by 109 · see citing works
Wang, C., Liou, Y.-A., Lin, Y., Chang, C., Chu, P., Lee, Y., Wang, C., & Chao, T. (2021) · Scientific Reports · doi:10.1038/s41598-021-95545-y · Cited by 97 · see citing works
A diagnostic prediction model for chronic kidney disease in internet of things platform
Hosseinzadeh, M., Koohpayehzadeh, J., Bali, A. O., Asghari, P., Souri, A., Mazaherinezhad, A., Bohlouli, M., & Rawassizadeh, R. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09049-4 · Cited by 91 · see citing works
Review of present method of glucose from human blood and body fluids assessment
Fiedorova, K., Augustýnek, M., Kubíček, J., Kudrna, P., & Bibbo, D. (2022) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2022.114348 · Cited by 93 · see citing works
Allen, A. O., Iqbal, Z., Green‐Saxena, A., Hurtado, M., Hoffman, J. L., Mao, Q., & Das, R. (2022) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2021-002560 · Cited by 80 · see citing works
Nagaraj, S. B., Pena, M. J., Ju, W., & Heerspink, H. J. L. (2020) · Diabetes Obesity and Metabolism · doi:10.1111/dom.14178 · Cited by 77 · see citing works
Analyzing Patient Trajectories With Artificial Intelligence
Allam, A., Feuerriegel, S., Rebhan, M., & Krauthammer, M. (2021) · Journal of Medical Internet Research · doi:10.2196/29812 · Cited by 77 · see citing works
Role of Artificial Intelligence in Kidney Disease
Yuan, Q., Zhang, H., Deng, T., Tang, S., Yuan, X., Tang, W., Xie, Y., Ge, H., Wang, X., Zhou, Q., et al. (2020) · International Journal of Medical Sciences · doi:10.7150/ijms.42078 · Cited by 81 · see citing works
Application of Machine Learning in Chronic Kidney Disease: Current Status and Future Prospects
Delrue, C., Bruyne, S. D., & Speeckaert, M. M. (2024) · Biomedicines · doi:10.3390/biomedicines12030568 · Cited by 76 · see citing works
Rubini, L. J., & Perumal, E. (2020) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22406 · Cited by 71 · see citing works
Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation
Thongprayoon, C., Kaewput, W., Kovvuru, K. R., Hansrivijit, P., Kanduri, S. R., Bathini, T. J., Chewcharat, A., Leeaphorn, N., Suarez, M. L. G., & Cheungpasitporn, W. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9041107 · Cited by 67 · see citing works
Wang, Z., Myles, P., & Tucker, A. (2021) · Computational Intelligence · doi:10.1111/coin.12427 · Cited by 65 · see citing works
Ma, C., Wang, X., Wu, J., Cheng, X., Xia, L., Xue, F., & Qiu, L. (2020) · Clinical Biochemistry · doi:10.1016/j.clinbiochem.2020.06.014 · Cited by 64 · see citing works
Active, continual fine tuning of convolutional neural networks for reducing annotation efforts
Zhou, Z., Shin, J. Y., Gurudu, S., Gotway, M. B., & Liang, J. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.101997 · Cited by 64 · see citing works
A multi-step approach for tongue image classification in patients with diabetes
Li, J., Huang, J.-B., Jiang, T., Tu, L., Cui, L., Cui, J. W., Ma, X., Yao, X., Shi, Y., Wang, S., et al. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105935 · Cited by 68 · see citing works
The upside of being a digital pharma player
Schuhmacher, A., Gatto, A., Hinder, M., Kuss, M., & Gassmann, O. (2020) · Drug Discovery Today · doi:10.1016/j.drudis.2020.06.002 · Cited by 63 · see citing works
Quantum Kernels for Real-World Predictions Based on Electronic Health Records
Krunic, Z., Flöther, F. F., Seegan, G., Earnest-Noble, N., & Shehab, O. (2022) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2022.3176806 · Cited by 65 · see citing works
Self-citationPredicting coronary heart disease in Chinese diabetics using machine learning
Ma, C.-Y., Luo, Y., Zhang, T., Hao, Y.-D., Xie, X., Liu, X., Ren, X., He, X.-L., Han, Y., Deng, K., et al. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.107952 · Cited by 66 · see citing works
Raket, L. L., Jaskolowski, J., Kinon, B. J., Brasen, J. C., Jönsson, L., Wehnert, A., & Fusar-Poli, P. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30024-8 · Cited by 60 · see citing works
Huang, J., Huth, C., Covic, M., Troll, M., Adam, J., Zukunft, S., Prehn, C., Wang, L., Nano, J., Scheerer, M. F., et al. (2020) · Diabetes · doi:10.2337/db20-0586 · Cited by 60 · see citing works
Tremblay, J., Haloui, M., Attaoua, R., Tahir, M. R., Hishmih, C., Harvey, F., Marois-Blanchet, F.-C., Long, C., Simon, P., Santucci, L., et al. (2021) · Diabetologia · doi:10.1007/s00125-021-05491-7 · Cited by 56 · see citing works
Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes
Huang, J.-T., Yeung, A., Armstrong, D., Battarbee, A., Cuadros, J. A., Espinoza, J. C., Kleinberg, S., Mathioudakis, N., Swerdlow, M. A., & Klonoff, D. (2022) · Journal of Diabetes Science and Technology · doi:10.1177/19322968221124583 · Cited by 47 · see citing works
Boutilier, J. J., Chan, T. C. Y., Ranjan, M., & Deo, S. (2021) · Journal of Medical Internet Research · doi:10.2196/20123 · Cited by 55 · see citing works
Sabanayagam, C., He, F., Nusinovici, S., Li, J., Lim, C., Tan, G., & Cheng, C. Y. (2023) · eLife · doi:10.7554/elife.81878 · Cited by 51 · see citing works
Modeling methodology for early warning of chronic heart failure based on real medical big data
Zhou, C., Li, A., Hou, A., Zhang, Z., Zhang, Z., Dai, P., & Wang, F. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113361 · Cited by 49 · see citing works
Abraham, A., Le, B. L., Kosti, I., Straub, P., Velez-Edwards, D. R., Davis, L. K., Newton, J. M., Muglia, L. J., Rokas, A., Bejan, C. A., et al. (2022) · BMC Medicine · doi:10.1186/s12916-022-02522-x · Cited by 47 · see citing works
Venkatrao, K., & Shaik, K. (2023) · IEEE Access · doi:10.1109/access.2023.3312183 · Cited by 47 · see citing works
Generating and Evaluating Synthetic UK Primary Care Data: Preserving Data Utility & Patient Privacy
Wang, Z., Myles, P. R., & Tucker, A. (2019) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2019.00036 · Cited by 42 · see citing works
Current trends in diabetes mellitus database research in Japan
Kohsaka, S., Morita, N., Okami, S., Kidani, Y., & Yajima, T. (2021) · Diabetes Obesity and Metabolism · doi:10.1111/dom.14325 · Cited by 42 · see citing works
A Semi-Supervised Multi-Task Learning Approach for Predicting Short-Term Kidney Disease Evolution
Bernardini, M., Romeo, L., Frontoni, E., & Amini, M.-R. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3074206 · Cited by 41 · see citing works
Huang, G. C., Wei, X., Tang, H., Bai, F., Lin, X., & Xue, D. (2021) · Journal of Thoracic Disease · doi:10.21037/jtd-21-810 · Cited by 39 · see citing works
Big data in nephrology: Are we ready for the change?
Yang, C., Kong, G., Wang, L., Zhang, L., & Zhao, M. (2019) · Nephrology · doi:10.1111/nep.13636 · Cited by 38 · see citing works
Innovating Personalized Nephrology Care: Exploring the Potential Utilization of ChatGPT
Miao, J., Thongprayoon, C., Suppadungsuk, S., Valencia, O. A. G., Qureshi, F., & Cheungpasitporn, W. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13121681 · Cited by 37 · see citing works
Chicco, D., Lovejoy, C. A., & Oneto, L. (2021) · IEEE Access · doi:10.1109/access.2021.3133700 · Cited by 39 · see citing works
How quantum computing can enhance biomarker discovery
Flöther, F. F., Blankenberg, D. J., Demidik, M., Jansen, K., Krishnakumar, R., Krishnakumar, R., Laanait, N., Parida, L., Saab, C. Y., & Utro, F. (2025) · Patterns · doi:10.1016/j.patter.2025.101236 · Cited by 41 · see citing works
Self-citationApplications of machine learning methods in kidney disease
Chan, L., Vaid, A., & Nadkarni, G. N. (2020) · Current Opinion in Nephrology & Hypertension · doi:10.1097/mnh.0000000000000604 · Cited by 35 · see citing works
Application of artificial intelligence in renal disease
Yao, L., Zhang, H., Zhang, M., Chen, X., Zhang, J., Huang, J., & Zhang, L. (2021) · Clinical eHealth · doi:10.1016/j.ceh.2021.11.003 · Cited by 36 · see citing works
Gao, J., Liu, L., Gao, P., Zheng, Y., Hou, W., & Wang, J. (2019) · Sensors · doi:10.3390/s20010089 · Cited by 33 · see citing works
Deep Feature Selection and Causal Analysis of Alzheimer’s Disease
Liu, Y., Li, Z., Ge, Q., Lin, N., & Xiong, M. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.01198 · Cited by 32 · see citing works
Ringshausen, F. C., Ewen, R., Multmeier, J., Monga, B., Obradović, M., van der Laan, R., & Diel, R. (2021) · International Journal of Infectious Diseases · doi:10.1016/j.ijid.2021.01.003 · Cited by 32 · see citing works
Leveraging graph-based hierarchical medical entity embedding for healthcare applications
Wu, T., Wang, Y., Wang, Y., Zhao, E., & Yuan, Y. (2021) · Scientific Reports · doi:10.1038/s41598-021-85255-w · Cited by 31 · see citing works
Kaur, N., Bhattacharya, S., & Butte, A. J. (2021) · Nature Reviews Nephrology · doi:10.1038/s41581-021-00439-x · Cited by 33 · see citing works
Revolutionizing Chronic Kidney Disease Management with Machine Learning and Artificial Intelligence
Krisanapan, P., Tangpanithandee, S., Thongprayoon, C., Pattharanitima, P., & Cheungpasitporn, W. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12083018 · Cited by 31 · see citing works
Pan, Q., & Tong, M. (2024) · Renal Failure · doi:10.1080/0886022x.2024.2435483 · Cited by 29 · see citing works
Kanda, E., Epureanu, B. I., ADACHI, T., & Kashihara, N. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000188 · Cited by 27 · see citing works
Attitudes towards medical artificial intelligence talent cultivation: an online survey study
Yun, D., Xiang, Y., Liu, Z., Lin, D., Zhao, L., Guo, C., Xie, P., Lin, H., Liu, Y., Zou, Y., et al. (2020) · Annals of Translational Medicine · doi:10.21037/atm.2019.12.149 · Cited by 27 · see citing works
Artificial intelligence and machine learning trends in kidney care
Ho, Y., Fülöp, T., Krisanapan, P., Soliman, K., & Cheungpasitporn, W. (2024) · The American Journal of the Medical Sciences · doi:10.1016/j.amjms.2024.01.018 · Cited by 26 · see citing works
Ma, C., Xia, L., Cheng, X., Wu, J., Yin, Y., Hou, L., Li, X., Guo, X., Lin, S., & Qiu, L. (2020) · Age and Ageing · doi:10.1093/ageing/afaa096 · Cited by 26 · see citing works
Machine learning in nephrology: scratching the surface
Li, Q., Fan, Q., Han, Q., Geng, W., Zhao, H., Ding, X., Yan, J.-Y., & Zhu, H. (2020) · Chinese Medical Journal · doi:10.1097/cm9.0000000000000694 · Cited by 25 · see citing works
Rinderknecht, M. D., & Klopfenstein, Y. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00482-9 · Cited by 23 · see citing works
Wang, G., Wang, K., Gao, Y., Chen, L., Gao, T., Ma, Y., Jiang, Z., Yang, G., Feng, F., Zhang, S., et al. (2024) · Patterns · doi:10.1016/j.patter.2024.100985 · Cited by 24 · see citing works
Ma, C., Cheng, X., Xue, F., Li, X., Yin, Y., Wu, J., Xia, L., Guo, X., Hu, Y., Qiu, L., et al. (2020) · Clinical Biochemistry · doi:10.1016/j.clinbiochem.2020.03.012 · Cited by 24 · see citing works
Tangri, N., & Ferguson, T. W. (2022) · Current Opinion in Nephrology & Hypertension · doi:10.1097/mnh.0000000000000787 · Cited by 24 · see citing works
Comparison of Different Machine Learning Techniques to Predict Diabetic Kidney Disease
David, S. K., Rafiullah, M., & Siddiqui, K. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/7378307 · Cited by 24 · see citing works
Predicting diabetic nephropathy in type 2 diabetic patients using machine learning algorithms
Hosseini Sarkhosh, S. M., Esteghamati, A., Hemmatabadi, M., & Daraei, M. (2022) · Journal of Diabetes & Metabolic Disorders · doi:10.1007/s40200-022-01076-2 · Cited by 24 · see citing works
Risk prediction in chronic kidney disease
Ali, I., & Kalra, P. A. (2019) · Current Opinion in Nephrology & Hypertension · doi:10.1097/mnh.0000000000000553 · Cited by 23 · see citing works
Mortality prediction with adaptive feature importance recalibration for peritoneal dialysis patients
Ma, L., Zhang, C., Gao, J., Jiao, X., Yu, Z., Zhu, Y., Wang, T., Ma, X., Wang, Y., Tang, W., et al. (2023) · Patterns · doi:10.1016/j.patter.2023.100892 · Cited by 23 · see citing works
Sun, L., Li, J., Zeng, S., Luo, Q., Miao, H., Liang, Y., Cheng, L., Sun, Z., Tai, W. H., Han, Y., et al. (2024) · Chinese Medical Journal · doi:10.1097/cm9.0000000000003162 · Cited by 22 · see citing works
Zou, X., Huang, Q., Luo, Y., Ren, Q., Han, X., Zhou, X., & Ji, L. (2022) · Diabetologia · doi:10.1007/s00125-022-05748-9 · Cited by 21 · see citing works
Wang, M., Sushil, M., Miao, B. Y., & Butte, A. J. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad085 · Cited by 20 · see citing works
Reddy, S., Roy, S., Choy, K. W., Sharma, S., Dwyer, K. M., Manapragada, C., Miller, Z., Cheon, J., & Nakisa, B. (2024) · Computer Methods and Programs in Biomedicine Update · doi:10.1016/j.cmpbup.2024.100160 · Cited by 16 · see citing works
Artificial Intelligence in the Identification, Management, and Follow-Up of CKD
Tangri, N., & Ferguson, T. W. (2022) · Kidney360 · doi:10.34067/kid.0007572021 · Cited by 16 · see citing works
Wang, Y., Lin, J., Bi, S., Sun, C., Si, L., & Liu, X. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116232 · Cited by 15 · see citing works
Brady, V., Whisenant, M., Wang, Y., Ly, V. K., Zhu, G. H., Aguilar, D. A., & Wu, H. (2022) · Diabetes Spectrum · doi:10.2337/ds21-0064 · Cited by 14 · see citing works
Wang, Y., Wang, S., Sima, X., Song, Y. P., Cui, S., & Wang, D. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110451 · Cited by 14 · see citing works
Data Science Methods for Real-World Evidence Generation in Real-World Data
Liu, F. (2024) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-102423-113220 · Cited by 15 · see citing works
Reddy, T. B. P., & Vydeki, D. (2022) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7446 · Cited by 14 · see citing works
Kanda, E., Okami, S., Kohsaka, S., Okada, M., Ma, X., Kimura, T., Shirakawa, K., & Yajima, T. (2022) · Nutrients · doi:10.3390/nu14214614 · Cited by 14 · see citing works
Chang, J., Chen, P., Hsieh, C., & Liou, J. (2021) · Diagnostics · doi:10.3390/diagnostics11020286 · Cited by 13 · see citing works
Tian, Y., Chen, W., Zhou, T., Li, J., Ding, K., & Li, J. (2020) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2020.104173 · Cited by 10 · see citing works
Wan, J., Chen, H., Xiao, L., Shen, C., Huang, W. X., & Li, X. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113913 · Cited by 10 · see citing works
Mesquita, F., Bernardino, J., Henriques, J., Raposo, J., Ribeiro, R., & Paredes, S. (2023) · Journal of Diabetes and Metabolic Disorders · doi:10.1007/s40200-023-01357-4 · Cited by 11 · see citing works
How wide is the application of genetic big data in biomedicine
Liu, Y., Li, N., Zhu, X. M., & Qi, Y. (2020) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2020.111074 · Cited by 10 · see citing works
Du, J., Zeng, D., Li, Z., Liu, J., Lv, M., Chen, L., Zhang, D., & Ji, S. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22697 · Cited by 10 · see citing works
Ma, J., Wang, J., Ying, J., Xie, S., Su, Q., Zhou, T., Han, F., Xu, J., Zhu, S., Yuan, C., et al. (2024) · Journal of Medical Internet Research · doi:10.2196/54206 · Cited by 9 · see citing works
Aminnejad, N., Greiver, M., & Huang, H. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0000700 · Cited by 8 · see citing works
Xu, H., Zheng, Q., Zhu, J., Xie, Z., Cheng, H., Li, P., & Ji, Y. (2022) · Disease Markers · doi:10.1155/2022/7593750 · Cited by 8 · see citing works
Williams, K. S., Rahurkar, S., Grannis, S. J., Schleyer, T. K., & Dixon, B. E. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-02933-9 · Cited by 7 · see citing works
Self-citationFrom bytes to nephrons: AI’s journey in diabetic kidney disease
Basuli, D., Kavcar, A. S., & Roy, S. (2024) · Journal of Nephrology · doi:10.1007/s40620-024-02050-2 · Cited by 7 · see citing works
Wang, Y., Gong, T., Hassan, M. A., Li, Q., Huang, S., & Zhou, Y. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11165-8 · Cited by 7 · see citing works
You, Y., & Hua, Z. (2022) · Information Sciences · doi:10.1016/j.ins.2022.07.080 · Cited by 7 · see citing works
Kandukuri, P., Abdul, A., Kumar, K. P., Sreenivas, V., Ramesh, G., & Gundu, V. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19926-x · Cited by 6 · see citing works
Xue, L., Zhang, Y., & Zhang, Q. (2025) · Frontiers in Endocrinology · doi:10.3389/fendo.2025.1468737 · Cited by 6 · see citing works
AI Integration in Behavioral Healthcare: A Practical Framework for Clinicians
Sadeh‐Sharvit, S., & Hollon, S. D. (2025) · Journal of Technology in Behavioral Science · doi:10.1007/s41347-025-00532-z · Cited by 7 · see citing works
Navarro‐González, J. F., de Isla, L. P., Molina, G. C., Brito-Sanfiel, M. Á., Galindo, D. E. B., Olmedo, L. Á. C., Mauricio, D., Tofé, S., Barro, J. A. B., Rubio-Almanza, M., et al. (2025) · Kidney Diseases · doi:10.1159/000547604 · Cited by 5 · see citing works
Assessing physical inactivity as a risk factor for chronic kidney diseases in Iranian population
Moeinzadeh, F., Babahajiani, M., Seirafian, S., Mansourian, M., Mortazavi, M., Shahidi, S., Vahdat, S., & Saleki, M. (2023) · BMJ Open · doi:10.1136/bmjopen-2022-070360 · Cited by 5 · see citing works
- Authors
- Ravizza, S., Huschto, T., Adamov, A., Böhm, L., Büsser, A., Flöther, F. F., Hinzmann, R., König, H., McAhren, S. M., Robertson, D. H., Schleyer, T., Schneidinger, B., & Petrich, W.
- Affiliations
- Roche Diabetes Care GmbH (Germany) (DE)
- Eli Lilly (United States) (US)
- Indiana Biosciences Research Institute (US)
- Regenstrief Institute (US)
- Corresponding author
- Petrich, Wolfgang
- ORCID
- Venue
- Nature Medicine
- Published
- 2019-01
- Volume
- 25
- Issue
- 1
- Pages
- 57-59
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W2900940968
- Cited by
- 167
- References
- 29
- Citations by year
- 2026: 14 · 2025: 17 · 2024: 22 · 2023: 18 · 2022: 27 · 2021: 31 · 2020: 28 · 2019: 10
- FWCI
- 9.72
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Medicine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Roche Diabetes Care
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Nephrology › Chronic Kidney Disease and Diabetes
- Keywords
- chronic kidney disease, diabetes mellitus, early risk prediction, real-world data, electronic health records, risk prediction algorithm, clinical features
- MeSH terms
- Data Analysis, Algorithms, Diabetes Mellitus, Humans, Prognosis, Sample Size, Area Under Curve, Renal Insufficiency, Chronic
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review