1566 related articles for article (PubMed ID: 37545445)
1. [Construction of a predictive model for in-hospital mortality of sepsis patients in intensive care unit based on machine learning].
Zhu M; Hu C; He Y; Qian Y; Tang S; Hu Q; Hao C
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2023 Jul; 35(7):696-701. PubMed ID: 37545445
[TBL] [Abstract][Full Text] [Related]
2. [Constructing a predictive model for the death risk of patients with septic shock based on supervised machine learning algorithms].
Xie Z; Jin J; Liu D; Lu S; Yu H; Han D; Sun W; Huang M
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2024 Apr; 36(4):345-352. PubMed ID: 38813626
[TBL] [Abstract][Full Text] [Related]
3. [Construction of a predictive model for early acute kidney injury risk in intensive care unit septic shock patients based on machine learning].
Zhang S; Tang S; Rong S; Zhu M; Liu J; Hu Q; Hao C
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2022 Mar; 34(3):255-259. PubMed ID: 35574741
[TBL] [Abstract][Full Text] [Related]
4. [Combined prognostic value of serum lactic acid, procalcitonin and severity score for short-term prognosis of septic shock patients].
Hao C; Hu Q; Zhu L; Xu H; Zhang Y
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2021 Mar; 33(3):281-285. PubMed ID: 33834968
[TBL] [Abstract][Full Text] [Related]
5. [A new warning scoring system establishment for prediction of sepsis in patients with trauma in intensive care unit].
Huang Q; Sun Y; Luo L; Meng S; Chen T; Ai S; Jiang D; Liang H
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2019 Apr; 31(4):422-427. PubMed ID: 31109414
[TBL] [Abstract][Full Text] [Related]
6. [Prognostic value of serum sodium variability within 72 hours and lactic acid combined with severity score in patients with sepsis].
Chi H; Wang H; Li Q; Lian Z; Zhang C; Zhang S; Hu D
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2023 May; 35(5):458-462. PubMed ID: 37308223
[TBL] [Abstract][Full Text] [Related]
7. [Comparison of four early warning scores in predicting the prognosis of critically ill patients in secondary hospitals].
Su X; Zhang H; Yuan W; Yi M; Fu C; Jiang J; Gao H
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2023 Oct; 35(10):1093-1098. PubMed ID: 37873716
[TBL] [Abstract][Full Text] [Related]
8. [Predictive value of four different scoring systems for septic patient's outcome: a retrospective analysis with 311 patients].
Wang S; Li T; Li Y; Zhang J; Dai X
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2017 Feb; 29(2):133-138. PubMed ID: 28625260
[TBL] [Abstract][Full Text] [Related]
9. Evaluate prognostic accuracy of SOFA component score for mortality among adults with sepsis by machine learning method.
Pan X; Xie J; Zhang L; Wang X; Zhang S; Zhuang Y; Lin X; Shi S; Shi S; Lin W
BMC Infect Dis; 2023 Feb; 23(1):76. PubMed ID: 36747139
[TBL] [Abstract][Full Text] [Related]
10. [Lactic acid, lactate clearance and procalcitonin in assessing the severity and predicting prognosis in sepsis].
Zhao M; Duan M
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2020 Apr; 32(4):449-453. PubMed ID: 32527351
[TBL] [Abstract][Full Text] [Related]
11. [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]
12. Construction and validation of machine learning models for sepsis prediction in patients with acute pancreatitis.
Liu F; Yao J; Liu C; Shou S
BMC Surg; 2023 Sep; 23(1):267. PubMed ID: 37658375
[TBL] [Abstract][Full Text] [Related]
13. Predicting in-hospital mortality in ICU patients with sepsis using gradient boosting decision tree.
Li K; Shi Q; Liu S; Xie Y; Liu J
Medicine (Baltimore); 2021 May; 100(19):e25813. PubMed ID: 34106618
[TBL] [Abstract][Full Text] [Related]
14. [Correlation between blood pressure indexes and prognosis in sepsis patients: a cohort study based on MIMIC-III database].
Liu X; Zhao Y; Qin Y; Ma Q; Wang Y; Weng Z; Zhu F
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2023 Jun; 35(6):578-585. PubMed ID: 37366122
[TBL] [Abstract][Full Text] [Related]
15. [Short-term survival survey and risk factors analysis of death in sepsis patients in intensive care unit].
Yao H; Shao H; Liu J; Zhang J; Liu D; Liu D
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2023 Oct; 35(10):1039-1044. PubMed ID: 37873707
[TBL] [Abstract][Full Text] [Related]
16. [Risk factors for death in elderly patients admitted to intensive care unit after elective abdominal surgery: a consecutive 5-year retrospective study].
Li S; He T; Shen F; Wang D; Liu X; Qin J; Xiao C; Li W; Li Q; Gao D
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2021 Dec; 33(12):1453-1458. PubMed ID: 35131012
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. [Prognostic value of coagulation function combined with acute physiology and chronic health evaluation II and sequential organ failure assessment scores for patients with bloodstream infection].
Yang M; Yang X; Jing P; Yang X; Zhao Z
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2021 Dec; 33(12):1434-1439. PubMed ID: 35131009
[TBL] [Abstract][Full Text] [Related]
19. [Establishment of risk prediction nomograph model for sepsis related acute respiratory distress syndrome].
Zhao C; Li Y; Wang Q; Yu G; Hu P; Zhang L; Liu M; Yuan H; You P
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2023 Jul; 35(7):714-718. PubMed ID: 37545448
[TBL] [Abstract][Full Text] [Related]
20. [Analysis of factors influencing recovery of renal functions in septic shock patients in intensive care unit with acute kidney injury].
He L; Su L; Zhang J; Peng Z
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2020 Feb; 32(2):199-203. PubMed ID: 32275006
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]