1. Abebe A, Kumela K, Belay M, Kebede B, Wobie Y. Mortality and predictors of acute kidney injury in adults: a hospital-based prospective observational study. Sci Rep 2021;11:15672.
2. Rewa O, Bagshaw SM. Acute kidney injury-epidemiology, outcomes and economics. Nat Rev Nephrol 2014;10:193–207.
3. Jung HY, Lee JH, Park YJ, et al. Duration of anuria predicts recovery of renal function after acute kidney injury requiring continuous renal replacement therapy. Korean J Intern Med 2016;31:930–937.
4. Kellum JA, Sileanu FE, Murugan R, Lucko N, Shaw AD, Clermont G. Classifying AKI by urine output versus serum creatinine level. J Am Soc Nephrol 2015;26:2231–2238.
5. Allen JC, Gardner DS, Skinner H, Harvey D, Sharman A, Devonald MAJ. Definition of hourly urine output influences reported incidence and staging of acute kidney injury. BMC Nephrol 2020;21:19.
6. Kang C, Han SH, Park JS, Choi DE. Risk factors for post-contrast acute kidney injury in patients sequentially administered iodine- and gadolinium-based contrast media on the same visit to the emergency department: a retrospective study. Kidney Res Clin Pract 2023;42:358–369.
7. Lee K, Jang HR. Role of T cells in ischemic acute kidney injury and repair. Korean J Intern Med 2022;37:534–550.
8. Patschan D, Erfurt S, Oess S, et al. Biomarker-based prediction of survival and recovery of kidney function in acute kidney injury. Kidney Blood Press Res 2023;48:124–134.
9. Jung HH. Albuminuria, estimated glomerular filtration rate, and traditional predictors for composite cardiovascular and kidney outcome: a population-based cohort study in Korea. Kidney Res Clin Pract 2022;41:567–579.
10. Kim Y, Kang E, Chae DW, et al. Insufficient early renal recovery and progression to subsequent chronic kidney disease in living kidney donors. Korean J Intern Med 2022;37:1021–1030.
11. Wen Y, Parikh CR. Current concepts and advances in biomarkers of acute kidney injury. Crit Rev Clin Lab Sci 2021;58:354–368.
12. Kellum JA, Bihorac A. Artificial intelligence to predict AKI: is it a breakthrough? Nat Rev Nephrol 2019;15:663–664.
13. Yoo JJ, Park MY, Kim SG. Acute kidney injury in patients with acute-on-chronic liver failure: clinical significance and management. Kidney Res Clin Pract 2023;42:286–297.
14. Gheisari M, Ebrahimzadeh F, Rahimi M, et al. Deep learning: applications, architectures, models, tools, and frameworks: a comprehensive survey. CAAI Trans Intell Technol 2023;8:581–606.
15. Koyner JL, Carey KA, Edelson DP, Churpek MM. The development of a machine learning inpatient acute kidney injury prediction model. Crit Care Med 2018;46:1070–1077.
16. Churpek MM, Carey KA, Edelson DP, et al. Internal and external validation of a machine learning risk score for acute kidney injury. JAMA Netw Open 2020;3:e2012892.
17. Pattharanitima P, Vaid A, Jaladanki SK, et al. Comparison of approaches for prediction of renal replacement therapy-free survival in patients with acute kidney injury. Blood Purif 2021;50:621–627.
18. Song X, Yu ASL, Kellum JA, et al. Cross-site transportability of an explainable artificial intelligence model for acute kidney injury prediction. Nat Commun 2020;11:5668.
19. Wirth FN, Meurers T, Johns M, Prasser F. Privacy-preserving data sharing infrastructures for medical research: systematization and comparison. BMC Med Inform Decis Mak 2021;21:242.
20. Johnson AE, Pollard TJ, Shen L, et al. MIMIC-III, a freely accessible critical care database. Sci Data 2016;3:160035.
22. Pollard TJ, Johnson AEW, Raffa JD, Celi LA, Mark RG, Badawi O. The eICU Collaborative Research Database, a freely available multi-center database for critical care research. Sci Data 2018;5:180178.
23. Wei S, Zhang Y, Dong H, et al. Machine learning-based prediction model of acute kidney injury in patients with acute respiratory distress syndrome. BMC Pulm Med 2023;23:370.
24. Yue S, Li S, Huang X, et al. Machine learning for the prediction of acute kidney injury in patients with sepsis. J Transl Med 2022;20:215.
25. Ko S, Jo C, Chang CB, et al. A web-based machine-learning algorithm predicting postoperative acute kidney injury after total knee arthroplasty. Knee Surg Sports Traumatol Arthrosc 2022;30:545–554.
26. Yu X, Wu R, Ji Y, Huang M, Feng Z. Identifying patients at risk of acute kidney injury among patients receiving immune checkpoint inhibitors: a machine learning approach. Diagnostics (Basel) 2022;12:3157.
27. Liu CL, Tain YL, Lin YC, Hsu CN. Prediction and Clinically important factors of acute kidney injury non-recovery. Front Med (Lausanne) 2022;8:789874.
28. Neyra JA, Ortiz-Soriano V, Liu LJ, et al. Prediction of mortality and major adverse kidney events in critically ill patients with acute kidney injury. Am J Kidney Dis 2023;81:36–47.
29. Jiang X, Hu Y, Guo S, Du C, Cheng X. Prediction of persistent acute kidney injury in postoperative intensive care unit patients using integrated machine learning: a retrospective cohort study. Sci Rep 2022;12:17134.
30. Li X, Wu R, Zhao W, et al. Machine learning algorithm to predict mortality in critically ill patients with sepsis-associated acute kidney injury. Sci Rep 2023;13:5223.
31. Alfieri F, Ancona A, Tripepi G, et al. A deep-learning model to continuously predict severe acute kidney injury based on urine output changes in critically ill patients. J Nephrol 2021;34:1875–1886.
32. Kim K, Yang H, Yi J, et al. Real-time clinical decision support based on recurrent neural networks for in-hospital acute kidney injury: external validation and model interpretation. J Med Internet Res 2021;23:e24120.
33. Li Y, Xu J, Wang Y, et al. A novel machine learning algorithm, Bayesian networks model, to predict the high-risk patients with cardiac surgery-associated acute kidney injury. Clin Cardiol 2020;43:752–761.
34. Khwaja A. KDIGO clinical practice guidelines for acute kidney injury. Nephron Clin Pract 2012;120:c179–c184.
35. Chawla LS, Bellomo R, Bihorac A, et al. Acute kidney disease and renal recovery: consensus report of the Acute Disease Quality Initiative (ADQI) 16 Workgroup. Nat Rev Nephrol 2017;13:241–257.
36. Kung CW, Chou YH. Acute kidney disease: an overview of the epidemiology, pathophysiology, and management. Kidney Res Clin Pract 2023;42:686–699.
37. Lameire NH, Levin A, Kellum JA, et al. Harmonizing acute and chronic kidney disease definition and classification: report of a Kidney Disease: Improving Global Outcomes (KDIGO) Consensus Conference. Kidney Int 2021;100:516–526.
38. Kim H. The new race-free equations for estimating glomerular filtration rate: should they be adopted for Asians? Kidney Res Clin Pract 2023;42:670–671.
39. Lee YJ, Park YS, Park SJ, Jhang WK. Estimating baseline creatinine values to define acute kidney injury in critically ill pediatric patients. Kidney Res Clin Pract 2022;41:322–331.
40. Mohamadlou H, Lynn-Palevsky A, Barton C, et al. Prediction of acute kidney injury with a machine learning algorithm using electronic health record data. Can J Kidney Health Dis 2018;5:2054358118776326.
41. Zhou Y, Feng J, Mei S, et al. Machine learning models for predicting acute kidney injury in patients with sepsis-associated acute respiratory distress syndrome. Shock 2023;59:352–359.
42. Jiang J, Liu X, Cheng Z, Liu Q, Xing W. Interpretable machine learning models for early prediction of acute kidney injury after cardiac surgery. BMC Nephrol 2023;24:326.
43. Zhang H, Wang AY, Wu S, et al. Artificial intelligence for the prediction of acute kidney injury during the perioperative period: systematic review and Meta-analysis of diagnostic test accuracy. BMC Nephrol 2022;23:405.
44. Kamel Rahimi A, Ghadimi M, van der Vegt AH, et al. Machine learning clinical prediction models for acute kidney injury: the impact of baseline creatinine on prediction efficacy. BMC Med Inform Decis Mak 2023;23:207.
45. Li Y, Yao L, Mao C, Srivastava A, Jiang X, Luo Y. Early prediction of acute kidney injury in critical care setting using clinical notes. Proceedings (IEEE Int Conf Bioinformatics Biomed) 2018;2018:683–686.
46. Zimmerman LP, Reyfman PA, Smith ADR, et al. Early prediction of acute kidney injury following ICU admission using a multivariate panel of physiological measurements. BMC Med Inform Decis Mak 2019;19(Suppl 1):16.
47. Sato N, Uchino E, Kojima R, Hiragi S, Yanagita M, Okuno Y. Prediction and visualization of acute kidney injury in intensive care unit using one-dimensional convolutional neural networks based on routinely collected data. Comput Methods Programs Biomed 2021;206:106129.
48. Le S, Allen A, Calvert J, et al. Convolutional Neural network model for intensive care unit acute kidney injury prediction. Kidney Int Rep 2021;6:1289–1298.
49. Zheng L, Lin Y, Fang K, Wu J, Zheng M. Derivation and validation of a risk score to predict acute kidney injury in critically ill cirrhotic patients. Hepatol Res 2023;53:701–712.
50. He ZL, Zhou JB, Liu ZK, et al. Application of machine learning models for predicting acute kidney injury following donation after cardiac death liver transplantation. Hepatobiliary Pancreat Dis Int 2021;20:222–231.
51. Dong JF, Xue Q, Chen T, et al. Machine learning approach to predict acute kidney injury after liver surgery. World J Clin Cases 2021;9:11255–11264.
52. Zhang X, Chen S, Lai K, Chen Z, Wan J, Xu Y. Machine learning for the prediction of acute kidney injury in critical care patients with acute cerebrovascular disease. Ren Fail 2022;44:43–53.
53. Rice ML, Barreto EF, Rule AD, et al. Development and validation of a model to predict acute kidney injury following high-dose methotrexate in patients with lymphoma. Pharmacotherapy 2024;44:4–12.
54. Ma Z, Liu W, Deng F, et al. An early warning model to predict acute kidney injury in sepsis patients with prior hypertension. Heliyon 2024;10:e24227.
55. Zulu C, Mwaba C, Wa Somwe S. The renal angina index accurately predicts low risk of developing severe acute kidney injury among children admitted to a low-resource pediatric intensive care unit. Ren Fail 2023;45:2252095.
56. Wu M, Jiang X, Du K, Xu Y, Zhang W. Ensemble machine learning algorithm for predicting acute kidney injury in patients admitted to the neurointensive care unit following brain surgery. Sci Rep 2023;13:6705.
57. Chen Q, Zhang Y, Zhang M, Li Z, Liu J. Application of machine learning algorithms to predict acute kidney injury in elderly orthopedic postoperative patients. Clin Interv Aging 2022;17:317–330.
58. Huang CY, Güiza F, De Vlieger G, et al. Development and validation of clinical prediction models for acute kidney injury recovery at hospital discharge in critically ill adults. J Clin Monit Comput 2023;37:113–125.
59. Sun S, Annadi RR, Chaudhri I, et al. Short- and long-term recovery after moderate/severe AKI in patients with and without COVID-19. Kidney360 2021;3:242–257.
60. Rank N, Pfahringer B, Kempfert J, et al. Deep-learning-based real-time prediction of acute kidney injury outperforms human predictive performance. NPJ Digit Med 2020;3:139.
61. Henry KE, Kornfield R, Sridharan A, et al. Human-machine teaming is key to AI adoption: clinicians’ experiences with a deployed machine learning system. NPJ Digit Med 2022;5:97.
62. Vinisha FA, Sujihelen L. Study on missing values and outlier detection in concurrence with data quality enhancement for efficient data processing. In : Proceedings of the 2022, 4th International Conference on Smart Systems and Inventive Technology (ICSSIT); 2022 Jan 20–22; Tirunelveli: IEEE, 2022. p. 1600–1607.
63. Hoogland J, van Barreveld M, Debray TPA, et al. Handling missing predictor values when validating and applying a prediction model to new patients. Stat Med 2020;39:3591–3607.
64. Shawwa K, Ghosh E, Lanius S, Schwager E, Eshelman L, Kashani KB. Predicting acute kidney injury in critically ill patients using comorbid conditions utilizing machine learning. Clin Kidney J 2020;14:1428–1435.
65. Yang J, Peng H, Luo Y, Zhu T, Xie L. Explainable ensemble machine learning model for prediction of 28–day mortality risk in patients with sepsis-associated acute kidney injury. Front Med (Lausanne) 2023;10:1165129.
66. Peng C, Yang F, Li L, et al. A machine learning approach for the prediction of severe acute kidney injury following traumatic brain injury. Neurocrit Care 2023;38:335–344.
67. Luo XQ, Yan P, Zhang NY, et al. Machine learning for early discrimination between transient and persistent acute kidney injury in critically ill patients with sepsis. Sci Rep 2021;11:20269.
68. Perez-Lebel A, Varoquaux G, Le Morvan M, Josse J, Poline JB. Benchmarking missing-values approaches for predictive models on health databases. Gigascience 2022;11:giac013.
69. Liu Y, Qin S, Yepes AJ, Shao W, Zhang Z, Salim FD. Integrated convolutional and recurrent neural networks for health risk prediction using patient journey data with many missing values. In : Proceedings of the 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM); 2022 Dec 6–8; Las Vegas (NV): IEEE, 2022:p. 1658–1663.
70. Zhao X, Lu Y, Li S, et al. Predicting renal function recovery and short-term reversibility among acute kidney injury patients in the ICU: comparison of machine learning methods and conventional regression. Ren Fail 2022;44:1326–1337.
71. Yang Y, Xiao W, Liu X, Zhang Y, Jin X, Li X. Machine learning-assisted ensemble analysis for the prediction of acute pancreatitis with acute kidney injury. Int J Gen Med 2022;15:5061–5072.
72. Che Z, Purushotham S, Cho K, Sontag D, Liu Y. Recurrent neural networks for multivariate time series with missing values. Sci Rep 2018;8:6085.
73. Nijman SWJ, Hoogland J, Groenhof TKJ, et al. Real-time imputation of missing predictor values in clinical practice. Eur Heart J Digit Health 2020;2:154–164.
74. Nijman SWJ, Groenhof TKJ, Hoogland J, et al. Real-time imputation of missing predictor values improved the application of prediction models in daily practice. J Clin Epidemiol 2021;134:22–34.
75. Montesinos López OA, Montesinos López A, Crossa J. Overfitting, model tuning, and evaluation of prediction performance. In: Montesinos López OA, Montesinos López A, Crossa J, eds. Multivariate statistical machine learning methods for genomic prediction. Cham: Springer, 2022;109–139.
76. Ying X. An overview of overfitting and its solutions. J Phys Conf Ser 2019;1168:022022.
77. Minvielle E, Fourcade A, Ricketts T, Waelli M. Current developments in delivering customized care: a scoping review. BMC Health Serv Res 2021;21:575.
78. Varoquaux G, Cheplygina V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ Digit Med 2022;5:48.
79. Nadim MK, Forni LG, Mehta RL, et al. COVID-19-associated acute kidney injury: consensus report of the 25th Acute Disease Quality Initiative (ADQI) Workgroup. Nat Rev Nephrol 2020;16:747–764.
80. Bravata DM, Myers LJ, Perkins AJ, et al. Heterogeneity in COVID-19 patient volume, characteristics and outcomes across US Department of Veterans Affairs facilities: an observational cohort study. BMJ Open 2021;11:e044646.
81. Gao X, Ninan J, Bohman JK, et al. Extracorporeal membrane oxygenation and acute kidney injury: a single-center retrospective cohort. Sci Rep 2023;13:15112.
82. Zamirpour S, Hubbard AE, Feng J, Butte AJ, Pirracchio R, Bishara A. Development of a machine learning
Comments (0)