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Study Title and Description

Development and validation of a machine learning algorithm and hybrid system to predict the need for life-saving interventions in trauma patients.



Key Questions Addressed
1 Key Question 1: For patients with known or suspected trauma who are treated out-of-hospital by EMS personnel, what is the predictive utility of measures of circulatory compromise or derivative measures (e.g., the shock index) for predicting serious injury requiring transport to the highest level trauma center available? 1a: How does the predictive utility of the studied measures of circulatory compromise vary across age groups (e.g., children or the elderly)? Specifically, what values for the different age ranges are supported by the evidence?
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2 Key Question 2: For patients with known or suspected trauma who are treated out-of-hospital by EMS personnel, what is the predictive utility of measures of respiratory compromise for predicting serious injury requiring transport to the highest level trauma center available? 2a: How does the predictive utility of the studied measures of respiratory compromise vary across age groups (e.g., children or the elderly)? Specifically, what values for the different age ranges are supported by the evidence?
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Primary Publication Information
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TitleData
Title Development and validation of a machine learning algorithm and hybrid system to predict the need for life-saving interventions in trauma patients.
Author Liu NT., Holcomb JB., Wade CE., Batchinsky AI., Cancio LC., Darrah MI., Salinas J.
Country US Army Institute of Surgical Research, 3650 Chambers Pass, Building 3610, Fort Sam Houston, TX 78234-6315, USA, nehemiah.liu@us.army.mil.
Year 2014
Numbers Pubmed ID: 24263362

Secondary Publication Information
There are currently no secondary publications defined for this study.


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