176 related articles for article (PubMed ID: 34024283)
1. Intelligent prediction of RBC demand in trauma patients using decision tree methods.
Feng YN; Xu ZH; Liu JT; Sun XL; Wang DQ; Yu Y
Mil Med Res; 2021 May; 8(1):33. PubMed ID: 34024283
[TBL] [Abstract][Full Text] [Related]
2. Trends of Hemoglobin Oximetry: Do They Help Predict Blood Transfusion During Trauma Patient Resuscitation?
Yang S; Hu PF; Anazodo A; Gao C; Chen H; Wade C; Hartsky L; Miller C; Imle C; Fang R; Mackenzie CF
Anesth Analg; 2016 Jan; 122(1):115-25. PubMed ID: 26683104
[TBL] [Abstract][Full Text] [Related]
3. Prehospital parameters can help to predict coagulopathy and massive transfusion in trauma patients.
David JS; Voiglio EJ; Cesareo E; Vassal O; Decullier E; Gueugniaud PY; Peyrefitte S; Tazarourte K
Vox Sang; 2017 Aug; 112(6):557-566. PubMed ID: 28612932
[TBL] [Abstract][Full Text] [Related]
4. Prediction of Massive Transfusion in Trauma Patients with Shock Index, Modified Shock Index, and Age Shock Index.
Rau CS; Wu SC; Kuo SC; Pao-Jen K; Shiun-Yuan H; Chen YC; Hsieh HY; Hsieh CH; Liu HT
Int J Environ Res Public Health; 2016 Jul; 13(7):. PubMed ID: 27399737
[TBL] [Abstract][Full Text] [Related]
5. Application of a recursive partitioning decision tree algorithm for the prediction of massive transfusion in civilian trauma: the MTPitt prediction tool.
Seheult JN; Anto VP; Farhat N; Stram MN; Spinella PC; Alarcon L; Sperry J; Triulzi DJ; Yazer MH
Transfusion; 2019 Mar; 59(3):953-964. PubMed ID: 30548461
[TBL] [Abstract][Full Text] [Related]
6. [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]
7. Early predictors for massive transfusion in older adult severe trauma patients.
Ohmori T; Kitamura T; Ishihara J; Onishi H; Nojima T; Yamamoto K; Tamura R; Muranishi K; Matsumoto T; Tokioka T
Injury; 2017 May; 48(5):1006-1012. PubMed ID: 28063676
[TBL] [Abstract][Full Text] [Related]
8. Accuracy of shock index versus ABC score to predict need for massive transfusion in trauma patients.
Schroll R; Swift D; Tatum D; Couch S; Heaney JB; Llado-Farrulla M; Zucker S; Gill F; Brown G; Buffin N; Duchesne J
Injury; 2018 Jan; 49(1):15-19. PubMed ID: 29017765
[TBL] [Abstract][Full Text] [Related]
9. Accuracy of continuous noninvasive hemoglobin monitoring for the prediction of blood transfusions in trauma patients.
Galvagno SM; Hu P; Yang S; Gao C; Hanna D; Shackelford S; Mackenzie C
J Clin Monit Comput; 2015 Dec; 29(6):815-21. PubMed ID: 25753142
[TBL] [Abstract][Full Text] [Related]
10. Application of machine learning model in predicting the likelihood of blood transfusion after hip fracture surgery.
Chen X; Pan J; Li Y; Tang R
Aging Clin Exp Res; 2023 Nov; 35(11):2643-2656. PubMed ID: 37733228
[TBL] [Abstract][Full Text] [Related]
11. Machine learning for predicting preoperative red blood cell demand.
Feng Y; Xu Z; Sun X; Wang D; Yu Y
Transfus Med; 2021 Aug; 31(4):262-270. PubMed ID: 34028930
[TBL] [Abstract][Full Text] [Related]
12. Emergency department triage prediction of clinical outcomes using machine learning models.
Raita Y; Goto T; Faridi MK; Brown DFM; Camargo CA; Hasegawa K
Crit Care; 2019 Feb; 23(1):64. PubMed ID: 30795786
[TBL] [Abstract][Full Text] [Related]
13. A Machine Learning-Based Model to Predict Acute Traumatic Coagulopathy in Trauma Patients Upon Emergency Hospitalization.
Li K; Wu H; Pan F; Chen L; Feng C; Liu Y; Hui H; Cai X; Che H; Ma Y; Li T
Clin Appl Thromb Hemost; 2020; 26():1076029619897827. PubMed ID: 31908189
[TBL] [Abstract][Full Text] [Related]
14. Dynamic multi-outcome prediction after injury: Applying adaptive machine learning for precision medicine in trauma.
Christie SA; Conroy AS; Callcut RA; Hubbard AE; Cohen MJ
PLoS One; 2019; 14(4):e0213836. PubMed ID: 30970030
[TBL] [Abstract][Full Text] [Related]
15. Prediction of intraoperative red blood cell transfusion in valve replacement surgery: machine learning algorithm development based on non-anemic cohort.
Zhou R; Li Z; Liu J; Qian D; Meng X; Guan L; Sun X; Li H; Yu M
Front Cardiovasc Med; 2024; 11():1344170. PubMed ID: 38486703
[TBL] [Abstract][Full Text] [Related]
16. [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]
17. The Sydney Triage to Admission Risk Tool (START2) using machine learning techniques to support disposition decision-making.
Rendell K; Koprinska I; Kyme A; Ebker-White AA; Dinh MM
Emerg Med Australas; 2019 Jun; 31(3):429-435. PubMed ID: 30469164
[TBL] [Abstract][Full Text] [Related]
18. Application and Comparison of Laboratory Parameters for Forecasting Severe Hand-Foot-Mouth Disease Using Logistic Regression, Discriminant Analysis and Decision Tree.
Sui M; Huang X; Li Y; Ma X; Zhang C; Li X; Chen Z; Feng H; Ren J; Wang F; Xu B; Duan G
Clin Lab; 2016; 62(6):1023-31. PubMed ID: 27468564
[TBL] [Abstract][Full Text] [Related]
19. [Research on grading prediction model of traumatic hemorrhage volume based on deep learning].
Guo C; Han Y; Gong M; Zhang H; Wang J; Zhang R; Lu B; Li C; Li T
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2022 Jul; 34(7):746-751. PubMed ID: 36100415
[TBL] [Abstract][Full Text] [Related]
20. Comparison of massive and emergency transfusion prediction scoring systems after trauma with a new Bleeding Risk Index score applied in-flight.
Yang S; Mackenzie CF; Rock P; Lin C; Floccare D; Scalea T; Stumpf F; Winans C; Galvagno S; Miller C; Stein D; Hu PF
J Trauma Acute Care Surg; 2021 Feb; 90(2):268-273. PubMed ID: 33502145
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]