378 related articles for article (PubMed ID: 37275353)
1. Explainable ensemble machine learning model for prediction of 28-day mortality risk in patients with sepsis-associated acute kidney injury.
Yang J; Peng H; Luo Y; Zhu T; Xie L
Front Med (Lausanne); 2023; 10():1165129. PubMed ID: 37275353
[TBL] [Abstract][Full Text] [Related]
2. Construction and validation of prognostic models in critically Ill patients with sepsis-associated acute kidney injury: interpretable machine learning approach.
Fan Z; Jiang J; Xiao C; Chen Y; Xia Q; Wang J; Fang M; Wu Z; Chen F
J Transl Med; 2023 Jun; 21(1):406. PubMed ID: 37349774
[TBL] [Abstract][Full Text] [Related]
3. Machine learning for the prediction of acute kidney injury in patients with sepsis.
Yue S; Li S; Huang X; Liu J; Hou X; Zhao Y; Niu D; Wang Y; Tan W; Wu J
J Transl Med; 2022 May; 20(1):215. PubMed ID: 35562803
[TBL] [Abstract][Full Text] [Related]
4. Interpretable machine learning model for early prediction of 28-day mortality in ICU patients with sepsis-induced coagulopathy: development and validation.
Zhou S; Lu Z; Liu Y; Wang M; Zhou W; Cui X; Zhang J; Xiao W; Hua T; Zhu H; Yang M
Eur J Med Res; 2024 Jan; 29(1):14. PubMed ID: 38172962
[TBL] [Abstract][Full Text] [Related]
5. [Comparison of machine learning and Logistic regression model in predicting acute kidney injury after cardiac surgery: data analysis based on MIMIC-III database].
Xiong W; Zhang L; She K; Xu G; Bai S; Liu X
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2022 Nov; 34(11):1188-1193. PubMed ID: 36567564
[TBL] [Abstract][Full Text] [Related]
6. Machine learning-based prediction of in-hospital mortality for critically ill patients with sepsis-associated acute kidney injury.
Gao T; Nong Z; Luo Y; Mo M; Chen Z; Yang Z; Pan L
Ren Fail; 2024 Dec; 46(1):2316267. PubMed ID: 38369749
[TBL] [Abstract][Full Text] [Related]
7. Predicting sepsis in-hospital mortality with machine learning: a multi-center study using clinical and inflammatory biomarkers.
Zhang G; Shao F; Yuan W; Wu J; Qi X; Gao J; Shao R; Tang Z; Wang T
Eur J Med Res; 2024 Mar; 29(1):156. PubMed ID: 38448999
[TBL] [Abstract][Full Text] [Related]
8. Machine Learning for Predicting Risk and Prognosis of Acute Kidney Disease in Critically Ill Elderly Patients During Hospitalization: Internet-Based and Interpretable Model Study.
Li M; Han S; Liang F; Hu C; Zhang B; Hou Q; Zhao S
J Med Internet Res; 2024 May; 26():e51354. PubMed ID: 38691403
[TBL] [Abstract][Full Text] [Related]
9. Interpretable machine learning for 28-day all-cause in-hospital mortality prediction in critically ill patients with heart failure combined with hypertension: A retrospective cohort study based on medical information mart for intensive care database-IV and eICU databases.
Peng S; Huang J; Liu X; Deng J; Sun C; Tang J; Chen H; Cao W; Wang W; Duan X; Luo X; Peng S
Front Cardiovasc Med; 2022; 9():994359. PubMed ID: 36312291
[TBL] [Abstract][Full Text] [Related]
10. Application of interpretable machine learning for early prediction of prognosis in acute kidney injury.
Hu C; Tan Q; Zhang Q; Li Y; Wang F; Zou X; Peng Z
Comput Struct Biotechnol J; 2022; 20():2861-2870. PubMed ID: 35765651
[TBL] [Abstract][Full Text] [Related]
11. Predicting acute kidney injury risk in acute myocardial infarction patients: An artificial intelligence model using medical information mart for intensive care databases.
Cai D; Xiao T; Zou A; Mao L; Chi B; Wang Y; Wang Q; Ji Y; Sun L
Front Cardiovasc Med; 2022; 9():964894. PubMed ID: 36158815
[TBL] [Abstract][Full Text] [Related]
12. Development and Validation of Machine Learning Models for Real-Time Mortality Prediction in Critically Ill Patients With Sepsis-Associated Acute Kidney Injury.
Luo XQ; Yan P; Duan SB; Kang YX; Deng YH; Liu Q; Wu T; Wu X
Front Med (Lausanne); 2022; 9():853102. PubMed ID: 35783603
[TBL] [Abstract][Full Text] [Related]
13. Machine learning for the prediction of all-cause mortality in patients with sepsis-associated acute kidney injury during hospitalization.
Zhou H; Liu L; Zhao Q; Jin X; Peng Z; Wang W; Huang L; Xie Y; Xu H; Tao L; Xiao X; Nie W; Liu F; Li L; Yuan Q
Front Immunol; 2023; 14():1140755. PubMed ID: 37077912
[TBL] [Abstract][Full Text] [Related]
14. An interpretable machine learning model for predicting 28-day mortality in patients with sepsis-associated liver injury.
Wen C; Zhang X; Li Y; Xiao W; Hu Q; Lei X; Xu T; Liang S; Gao X; Zhang C; Yu Z; Lü M
PLoS One; 2024; 19(5):e0303469. PubMed ID: 38768153
[TBL] [Abstract][Full Text] [Related]
15. The prediction of in-hospital mortality in chronic kidney disease patients with coronary artery disease using machine learning models.
Ye Z; An S; Gao Y; Xie E; Zhao X; Guo Z; Li Y; Shen N; Ren J; Zheng J
Eur J Med Res; 2023 Jan; 28(1):33. PubMed ID: 36653875
[TBL] [Abstract][Full Text] [Related]
16. Prediction of acute kidney injury in patients with femoral neck fracture utilizing machine learning.
Liu J; Xu L; Zhu E; Han C; Ai Z
Front Surg; 2022; 9():928750. PubMed ID: 35959132
[TBL] [Abstract][Full Text] [Related]
17. [Application of support vector machine in predicting in-hospital mortality risk of patients with acute kidney injury in ICU].
Lin K; Xie JQ; Hu YH; Kong GL
Beijing Da Xue Xue Bao Yi Xue Ban; 2018 Apr; 50(2):239-244. PubMed ID: 29643521
[TBL] [Abstract][Full Text] [Related]
18. Interpretable machine learning model for early prediction of delirium in elderly patients following intensive care unit admission: a derivation and validation study.
Tang D; Ma C; Xu Y
Front Med (Lausanne); 2024; 11():1399848. PubMed ID: 38828233
[TBL] [Abstract][Full Text] [Related]
19. AKIML
Sun T; Yue X; Zhang G; Lin Q; Chen X; Huang T; Li X; Liu W; Tao Z
Clin Chim Acta; 2024 Jun; 559():119705. PubMed ID: 38702035
[TBL] [Abstract][Full Text] [Related]
20. Machine Learning-Based Prediction of Acute Kidney Injury Following Pediatric Cardiac Surgery: Model Development and Validation Study.
Luo XQ; Kang YX; Duan SB; Yan P; Song GB; Zhang NY; Yang SK; Li JX; Zhang H
J Med Internet Res; 2023 Jan; 25():e41142. PubMed ID: 36603200
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]