Machine Learning for Predicting Risk and Prognosis of Acute Kidney Disease in Critically Ill Elderly Patients During Hospitalization: Internet-Based and Interpretable Model Study

Journals

1. Shen L, Wu J, Lan J, Chen C, Wang Y, Li Z. Interpretable machine learning-based prediction of 28-day mortality in ICU patients with sepsis: a multicenter retrospective study. Frontiers in Cellular and Infection Microbiology 2025;14 View
2. Guo Y, Wang F, Ma S, Mao Z, Zhao S, Sui L, Jiao C, Lu R, Zhu X, Pan X. Relationship between atherogenic index of plasma and length of stay in critically ill patients with atherosclerotic cardiovascular disease: a retrospective cohort study and predictive modeling based on machine learning. Cardiovascular Diabetology 2025;24(1) View
3. Lee H, Kim Y, Kim J, Kim S, Jeong T. Optimizing Initial Vancomycin Dosing in Hospitalized Patients Using Machine Learning Approach for Enhanced Therapeutic Outcomes: Algorithm Development and Validation Study. Journal of Medical Internet Research 2025;27:e63983 View
4. Popoff B, Cabon S, Cuggia M, Bouzillé G, Clavier T. Expectations of Intensive Care Physicians Regarding an AI-Based Decision Support System for Weaning From Continuous Renal Replacement Therapy: Predevelopment Survey Study. JMIR Medical Informatics 2025;13:e63709 View
5. Chen B, Chen J, Huang H, Yan L, Lin L, Huang H. Admission hematocrit and fluctuating blood urea nitrogen levels predict the efficacy of blood purification treatment in severe acute pancreatitis patients. Journal of Artificial Organs 2025;28(3):431 View
6. Ma M, Liu J, Li C, Chen Y, Jia H, Hou A, Xu H. Thirty-day mortality risk prediction for geriatric patients undergoing non-cardiac surgery in the surgical intensive care unit. European Journal of Medical Research 2025;30(1) View
7. Tong Y, Wen K, Li E, Ai F, Tang P, Wen H, Guo B. Construction and Validation of a Machine Learning-Based Risk Prediction Model for Sleep Quality in Patients with OSA. Nature and Science of Sleep 2025;Volume 17:1271 View
8. Shi T, Lin Y, Zhao H, Kong G. Artificial intelligence models for predicting acute kidney injury in the intensive care unit: a systematic review of modeling methods, data utilization, and clinical applicability. JAMIA Open 2025;8(4) View
9. Jin J, Yu L, Zhou Q, Du Q, Nie X, Yin H, Gu W. Development and validation of a multidimensional predictive model for 28-day mortality in ICU patients with bloodstream infections: a cohort study. Frontiers in Cellular and Infection Microbiology 2025;15 View
10. Xiong Y, Cai X, Lai X, Wang Y, Xin H, Song W, Lv F, Guo X, Yang G, Wu Y. Real-world data-driven early warning system for risk-stratified liver injury in hospitalized COVID-19 patients—Machine learning models for clinical decision support. Frontiers in Public Health 2025;13 View
11. Xie M, Zhang Y, Wu H, Wu Z, Han H, Xie X, Zhang R, Cheng J, Xu J. Correlation of triglyceride-glucose index with the incidence and prognosis of hyperglycemic crises in critically ill patients with diabetes mellitus: a machine-learning-based multicenter retrospective cohort study. Frontiers in Nutrition 2025;12 View
12. Song X, Shi J, Zhu C, Xian F, Dong Z, Li J. XGBoost machine learning algorithm for predicting unplanned readmission in elderly patients with coronary heart disease. Geriatric Nursing 2025;66:103609 View
13. Ge X, Chen W, Shi J, Zhang J, Tai H, Zhang Y, Wang B, Liu W, Chen S, Han H. Prediction of Moderate-to-Severe Sepsis-Associated Acute Kidney Injury Using a Dual-Timepoint Machine Learning Model: Development, Multiregional Validation, and Clinical Deployment Study. Journal of Medical Internet Research 2025;27:e73840 View
14. Wu Z, Li M, Xu Z, Liu G. Machine learning model development and validation using SHAP: predicting 28-day mortality risk in pulmonary fibrosis patients. BMC Medical Informatics and Decision Making 2025;25(1) View
15. Wu D, Wan N. Machine learning identifies immune-perinatal predictors of infantile hemangioma. Frontiers in Pediatrics 2025;13 View