Intelligent prediction of RBC demand in trauma patients using decision tree methods

Background The vital signs of trauma patients are complex and changeable, and the prediction of blood transfusion demand mainly depends on doctors’ experience and trauma scoring system; therefore, it cannot be accurately predicted. In this study, a machine learning decision tree algorithm [classific...

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Veröffentlicht in:	Military medical research Jg. 8; H. 1; S. 33 - 12
Hauptverfasser:	Feng, Yan-Nan, Xu, Zhen-Hua, Liu, Jun-Ting, Sun, Xiao-Lin, Wang, De-Qing, Yu, Yang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 24.05.2021 Springer Nature B.V BMC
Schlagworte:	Adult Area Under Curve Artificial intelligence Blood pressure Blood Transfusion - methods Blood Transfusion - statistics & numerical data Blood transfusions China Classification Decision Support Techniques Decision tree Decision Trees Emergency Medicine Erythrocytes Female Forecasting - methods Hemorrhagic shock Hospitals Humans Intelligent prediction Invasive parameters Laboratories Logistic Models Male Mathematical model Medical prognosis Medicine Medicine & Public Health Middle Aged Mortality Natural language processing Non-invasive parameters Patients Physicians ROC Curve Trauma Trauma care Variables Vital signs Wounds and Injuries - physiopathology Wounds and Injuries - therapy China Transfusion Intelligent prediction Mathematical model Non-invasive parameters Decision tree Invasive parameters Trauma
ISSN:	2054-9369, 2095-7467, 2054-9369
Online-Zugang:	Volltext
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