176 related articles for article (PubMed ID: 37759175)
1. Predicting sepsis onset in ICU using machine learning models: a systematic review and meta-analysis.
Yang Z; Cui X; Song Z
BMC Infect Dis; 2023 Sep; 23(1):635. PubMed ID: 37759175
[TBL] [Abstract][Full Text] [Related]
2. Machine learning for the prediction of sepsis-related death: a systematic review and meta-analysis.
Zhang Y; Xu W; Yang P; Zhang A
BMC Med Inform Decis Mak; 2023 Dec; 23(1):283. PubMed ID: 38082381
[TBL] [Abstract][Full Text] [Related]
3. Machine learning in predicting cardiac surgery-associated acute kidney injury: A systemic review and meta-analysis.
Song Z; Yang Z; Hou M; Shi X
Front Cardiovasc Med; 2022; 9():951881. PubMed ID: 36186995
[TBL] [Abstract][Full Text] [Related]
4. [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]
5. Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas.
Crider K; Williams J; Qi YP; Gutman J; Yeung L; Mai C; Finkelstain J; Mehta S; Pons-Duran C; Menéndez C; Moraleda C; Rogers L; Daniels K; Green P
Cochrane Database Syst Rev; 2022 Feb; 2(2022):. PubMed ID: 36321557
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. MACHINE LEARNING MODELS FOR PREDICTING ACUTE KIDNEY INJURY IN PATIENTS WITH SEPSIS-ASSOCIATED ACUTE RESPIRATORY DISTRESS SYNDROME.
Zhou Y; Feng J; Mei S; Zhong H; Tang R; Xing S; Gao Y; Xu Q; He Z
Shock; 2023 Mar; 59(3):352-359. PubMed ID: 36625493
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review.
Moor M; Rieck B; Horn M; Jutzeler CR; Borgwardt K
Front Med (Lausanne); 2021; 8():607952. PubMed ID: 34124082
[No Abstract] [Full Text] [Related]
10. 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]
11. Can Predictive Modeling Tools Identify Patients at High Risk of Prolonged Opioid Use After ACL Reconstruction?
Anderson AB; Grazal CF; Balazs GC; Potter BK; Dickens JF; Forsberg JA
Clin Orthop Relat Res; 2020 Jul; 478(7):0-1618. PubMed ID: 32282466
[TBL] [Abstract][Full Text] [Related]
12. Predictive value of machine learning for the risk of acute kidney injury (AKI) in hospital intensive care units (ICU) patients: a systematic review and meta-analysis.
Du YH; Guan CJ; Li LY; Gan P
PeerJ; 2023; 11():e16405. PubMed ID: 38034868
[TBL] [Abstract][Full Text] [Related]
13. Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis.
Wang Q; Chang Z; Liu X; Wang Y; Feng C; Ping Y; Feng X
J Med Internet Res; 2024 Jan; 26():e48527. PubMed ID: 38252469
[TBL] [Abstract][Full Text] [Related]
14. Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy.
Fleuren LM; Klausch TLT; Zwager CL; Schoonmade LJ; Guo T; Roggeveen LF; Swart EL; Girbes ARJ; Thoral P; Ercole A; Hoogendoorn M; Elbers PWG
Intensive Care Med; 2020 Mar; 46(3):383-400. PubMed ID: 31965266
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. 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]
17. A Machine Learning Model for Accurate Prediction of Sepsis in ICU Patients.
Wang D; Li J; Sun Y; Ding X; Zhang X; Liu S; Han B; Wang H; Duan X; Sun T
Front Public Health; 2021; 9():754348. PubMed ID: 34722452
[No Abstract] [Full Text] [Related]
18. 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]
19. Prediction of sepsis patients using machine learning approach: A meta-analysis.
Islam MM; Nasrin T; Walther BA; Wu CC; Yang HC; Li YC
Comput Methods Programs Biomed; 2019 Mar; 170():1-9. PubMed ID: 30712598
[TBL] [Abstract][Full Text] [Related]
20. A Time-Phased Machine Learning Model for Real-Time Prediction of Sepsis in Critical Care.
Li X; Xu X; Xie F; Xu X; Sun Y; Liu X; Jia X; Kang Y; Xie L; Wang F; Xie G
Crit Care Med; 2020 Oct; 48(10):e884-e888. PubMed ID: 32931194
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
