180 related articles for article (PubMed ID: 36321175)
1. Comparison of Mortality Predictive Models of Sepsis Patients Based on Machine Learning.
Wang ZY; Lan YS; Xu ZD; Gu YW; Li J
Chin Med Sci J; 2022 Sep; 37(3):201-209. PubMed ID: 36321175
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost.
Hou N; Li M; He L; Xie B; Wang L; Zhang R; Yu Y; Sun X; Pan Z; Wang K
J Transl Med; 2020 Dec; 18(1):462. PubMed ID: 33287854
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. 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]
6. Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU.
Kong G; Lin K; Hu Y
BMC Med Inform Decis Mak; 2020 Oct; 20(1):251. PubMed ID: 33008381
[TBL] [Abstract][Full Text] [Related]
7. Predicting Sepsis Mortality in a Population-Based National Database: Machine Learning Approach.
Park JY; Hsu TC; Hu JR; Chen CY; Hsu WT; Lee M; Ho J; Lee CC
J Med Internet Res; 2022 Apr; 24(4):e29982. PubMed ID: 35416785
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. [Comparison of machine learning method and logistic regression model in prediction of acute kidney injury in severely burned patients].
Tang CQ; Li JQ; Xu DY; Liu XB; Hou WJ; Lyu KY; Xiao SC; Xia ZF
Zhonghua Shao Shang Za Zhi; 2018 Jun; 34(6):343-348. PubMed ID: 29961290
[No Abstract] [Full Text] [Related]
10. Machine learning for prediction of in-hospital mortality in lung cancer patients admitted to intensive care unit.
Huang T; Le D; Yuan L; Xu S; Peng X
PLoS One; 2023; 18(1):e0280606. PubMed ID: 36701342
[TBL] [Abstract][Full Text] [Related]
11. Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data-Driven, Machine Learning Approach.
Taylor RA; Pare JR; Venkatesh AK; Mowafi H; Melnick ER; Fleischman W; Hall MK
Acad Emerg Med; 2016 Mar; 23(3):269-78. PubMed ID: 26679719
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. [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]
14. Machine learning approaches to predict peak demand days of cardiovascular admissions considering environmental exposure.
Qiu H; Luo L; Su Z; Zhou L; Wang L; Chen Y
BMC Med Inform Decis Mak; 2020 May; 20(1):83. PubMed ID: 32357880
[TBL] [Abstract][Full Text] [Related]
15. Using machine learning algorithms to predict 28-day mortality in critically ill elderly patients with colorectal cancer.
Guo C; Pan J; Tian S; Gao Y
J Int Med Res; 2023 Nov; 51(11):3000605231198725. PubMed ID: 37950672
[TBL] [Abstract][Full Text] [Related]
16. Machine learning models to predict in-hospital mortality in septic patients with diabetes.
Qi J; Lei J; Li N; Huang D; Liu H; Zhou K; Dai Z; Sun C
Front Endocrinol (Lausanne); 2022; 13():1034251. PubMed ID: 36465642
[TBL] [Abstract][Full Text] [Related]
17. [Predictive value of machine learning for in-hospital mortality for trauma-induced acute respiratory distress syndrome patients: an analysis using the data from MIMIC III].
Tang R; Tang W; Wang D
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2022 Mar; 34(3):260-264. PubMed ID: 35574742
[TBL] [Abstract][Full Text] [Related]
18. An intelligent warning model for early prediction of cardiac arrest in sepsis patients.
Layeghian Javan S; Sepehri MM; Layeghian Javan M; Khatibi T
Comput Methods Programs Biomed; 2019 Sep; 178():47-58. PubMed ID: 31416562
[TBL] [Abstract][Full Text] [Related]
19. Novel pneumonia score based on a machine learning model for predicting mortality in pneumonia patients on admission to the intensive care unit.
Wang B; Li Y; Tian Y; Ju C; Xu X; Pei S
Respir Med; 2023 Oct; 217():107363. PubMed ID: 37451647
[TBL] [Abstract][Full Text] [Related]
20. Machine learning is an effective method to predict the 90-day prognosis of patients with transient ischemic attack and minor stroke.
Chen SD; You J; Yang XM; Gu HQ; Huang XY; Liu H; Feng JF; Jiang Y; Wang YJ
BMC Med Res Methodol; 2022 Jul; 22(1):195. PubMed ID: 35842606
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]