Published on in Vol 22, No 8 (2020): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/16903, first published .
Machine Learning Model for Risk Prediction of Community-Acquired Acute Kidney Injury Hospitalization From Electronic Health Records: Development and Validation Study

Machine Learning Model for Risk Prediction of Community-Acquired Acute Kidney Injury Hospitalization From Electronic Health Records: Development and Validation Study

Machine Learning Model for Risk Prediction of Community-Acquired Acute Kidney Injury Hospitalization From Electronic Health Records: Development and Validation Study

Journals

  1. Liu J, Wu J, Liu S, Li M, Hu K, Li K, Adrish M. Predicting mortality of patients with acute kidney injury in the ICU using XGBoost model. PLOS ONE 2021;16(2):e0246306 View
  2. Domínguez-Olmedo J, Gragera-Martínez Á, Mata J, Pachón Álvarez V. Machine Learning Applied to Clinical Laboratory Data in Spain for COVID-19 Outcome Prediction: Model Development and Validation. Journal of Medical Internet Research 2021;23(4):e26211 View
  3. Li H, Niu X, Wang B. Prediction of Ecosystem Service Function of Grain for Green Project Based on Ensemble Learning. Forests 2021;12(5):537 View
  4. Zhang L, Niu M, Zhang H, Wang Y, Zhang H, Mao Z, Zhang X, He M, Wu T, Wang Z, Wang C. Nonlaboratory-based risk assessment model for coronary heart disease screening: Model development and validation. International Journal of Medical Informatics 2022;162:104746 View
  5. Alabbad D, Almuhaideb A, Alsunaidi S, Alqudaihi K, Alamoudi F, Alhobaishi M, Alaqeel N, Alshahrani M. Machine learning model for predicting the length of stay in the intensive care unit for Covid-19 patients in the eastern province of Saudi Arabia. Informatics in Medicine Unlocked 2022;30:100937 View
  6. Tain Y, Liu C, Kuo H, Hsu C. Kidney Function Trajectory within Six Months after Acute Kidney Injury Inpatient Care and Subsequent Adverse Kidney Outcomes: A Retrospective Cohort Study. Journal of Personalized Medicine 2022;12(10):1606 View
  7. Özbay Karakuş M, Er O. A comparative study on prediction of survival event of heart failure patients using machine learning algorithms. Neural Computing and Applications 2022;34(16):13895 View
  8. Liu C, Tain Y, Lin Y, Hsu C. Prediction and Clinically Important Factors of Acute Kidney Injury Non-recovery. Frontiers in Medicine 2022;8 View
  9. Okawa T, Mizuno T, Hanabusa S, Ikeda T, Mizokami F, Koseki T, Takahashi K, Yuzawa Y, Tsuboi N, Yamada S, Kameya Y, Mukhopadhyay P. Prediction model of acute kidney injury induced by cisplatin in older adults using a machine learning algorithm. PLOS ONE 2022;17(1):e0262021 View
  10. Sheu R, Pardeshi M. A Survey on Medical Explainable AI (XAI): Recent Progress, Explainability Approach, Human Interaction and Scoring System. Sensors 2022;22(20):8068 View
  11. Neyra J, Ortiz-Soriano V, Liu L, Smith T, Li X, Xie D, Adams-Huet B, Moe O, Toto R, Chen J. Prediction of Mortality and Major Adverse Kidney Events in Critically Ill Patients With Acute Kidney Injury. American Journal of Kidney Diseases 2023;81(1):36 View
  12. Selby N, Pannu N. Opportunities in digital health and electronic health records for acute kidney injury care. Current Opinion in Critical Care 2022;28(6):605 View
  13. Lee T, Chen J, Cheng C, Chang C. Does Artificial Intelligence Make Clinical Decision Better? A Review of Artificial Intelligence and Machine Learning in Acute Kidney Injury Prediction. Healthcare 2021;9(12):1662 View
  14. Vagliano I, Chesnaye N, Leopold J, Jager K, Abu-Hanna A, Schut M. Machine learning models for predicting acute kidney injury: a systematic review and critical appraisal. Clinical Kidney Journal 2022;15(12):2266 View
  15. Alanazi E, Abdou A, Luo J. Predicting Risk of Stroke From Lab Tests Using Machine Learning Algorithms: Development and Evaluation of Prediction Models. JMIR Formative Research 2021;5(12):e23440 View
  16. Chen H, Hsu C, Lee Y, Fu C, Wang S, Huang C, Li L. Comparative Adverse Kidney Outcomes in Women Receiving Raloxifene and Denosumab in a Real-World Setting. Biomedicines 2022;10(7):1494 View
  17. Zhang X, Xue Y, Su X, Chen S, Liu K, Chen W, Liu M, Hu Y. A Transfer Learning Approach to Correct the Temporal Performance Drift of Clinical Prediction Models: Retrospective Cohort Study. JMIR Medical Informatics 2022;10(11):e38053 View
  18. Deng Y, Luo X, Yan P, Zhang N, Liu Y, Duan S. Outcome prediction for acute kidney injury among hospitalized children via eXtreme Gradient Boosting algorithm. Scientific Reports 2022;12(1) View
  19. Pan P, Liu Y, Xie F, Duan Z, Li L, Gu H, Xie L, Lu X, Su L. Significance of platelets in the early warning of new-onset AKI in the ICU by using supervise learning: a retrospective analysis. Renal Failure 2023;45(1) View
  20. Feng Y, Wang A, Jun M, Pu L, Weisbord S, Bellomo R, Hong D, Gallagher M. Characterization of Risk Prediction Models for Acute Kidney Injury. JAMA Network Open 2023;6(5):e2313359 View
  21. Lu S, Porter I, Valderas J, Harrison C, Sidey-Gibbons C. Effectiveness of routine provision of feedback from patient‐reported outcome measurements for cancer care improvement: a systematic review and meta-analysis. Journal of Patient-Reported Outcomes 2023;7(1) View
  22. Shi H, Shen Y, Li L. Early prediction of acute kidney injury in patients with gastrointestinal bleeding admitted to the intensive care unit based on extreme gradient boosting. Frontiers in Medicine 2023;10 View
  23. Lee M, Heo K, Lee S, Ah Y, Shin J, Lee J. Development and validation of a medication-based risk prediction model for acute kidney injury in older outpatients. Archives of Gerontology and Geriatrics 2024;120:105332 View
  24. Liu Z, Lin S, Zhou J, Wang X, Wang Z, Yang Y, Ma H, Chen Z, Ren K, Wu L, Zhuang H, Ling Y, Feng T. Machine‐learning model for the prediction of acute orthostatic hypotension after levodopa administration. CNS Neuroscience & Therapeutics 2024;30(3) View
  25. Heo S, Kang E, Yu J, Kim H, Lee S, Kim K, Hwangbo Y, Park R, Shin H, Ryu K, Kim C, Jung H, Chegal Y, Lee J, Park Y. Development and Verification of a Time Series AI Model for Acute Kidney Injury Detection Based on a Multicenter Distributed Research Network (Preprint). JMIR Medical Informatics 2023 View
  26. Gracida-Osorno C, Molina-Salinas G, Góngora-Hernández R, Brito-Loeza C, Uc-Cachón A, Paniagua-Sierra J. Machine Learning for Predicting Chronic Renal Disease Progression in COVID-19 Patients with Acute Renal Injury: A Feasibility Study. Biomedicines 2024;12(7):1511 View

Books/Policy Documents

  1. Uchino E, Sato N, Okuno Y. Artificial Intelligence in Medicine. View
  2. Uchino E, Sato N, Okuno Y. Artificial Intelligence in Medicine. View
  3. Su J, Chiou T, Liao Y, Liao Y, Wu C, Lin W. Recent Challenges in Intelligent Information and Database Systems. View