Rule-based Computer Aided Decision Making for Traumatic Brain Injuries

  • Ashwin BelleEmail author
  • Soo-Yeon Ji
  • Wenan Chen
  • Toan Huynh
  • Kayvan Najarian
Part of the Intelligent Systems Reference Library book series (ISRL, volume 56)


This chapter provides an overview of various machine learning algorithms which are typically adopted into many predictive computer-assisted decision making systems for traumatic injuries. The objective here is to compare some existing machine learning methods using an aggregated database of traumatic injuries. These methods are used towards the development of rule-based computer-assisted decision-making systems that provide recommendations to physicians for the course of treatment of the patients. Since physicians in trauma centers are constantly required to make quick yet difficult decisions for patient care using a multitude of patient information, such computer assisted decision support systems are bound to play a vital role in improving healthcare. The content of this chapter also presents a novel image processing method to assess traumatic brain injuries (TBI).


Intensive Care Unit Support Vector Machine Traumatic Brain Injury Injury Severity Score Multivariate Adaptive Regression Spline 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Ashwin Belle
    • 1
    Email author
  • Soo-Yeon Ji
    • 2
  • Wenan Chen
    • 3
  • Toan Huynh
    • 4
  • Kayvan Najarian
    • 1
  1. 1.Department of Computer ScienceSchool of Engineering, Virginia Commonwealth UniversityRichmondUSA
  2. 2.Department of Computer ScienceBowie State UniversityBowieUSA
  3. 3.Department of BiostatisticsVirginia Commonwealth UniversityRichmondUSA
  4. 4.The Department of General Surgery, Division of TraumaSurgical Critical Care and Acute Care Surgery, Carolinas Medical CenterCharlotteUSA

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