Volume 1598 of the series Methods in Molecular Biology pp 405-419


An Integration of Decision Tree and Visual Analysis to Analyze Intracranial Pressure

  • Soo-Yeon JiAffiliated withDepartment of Computer Science, Bowie State University Email author 
  • , Kayvan NajarianAffiliated withDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor
  • , Toan HuynhAffiliated withDivision of Trauma, Surgical Critical Care, Carolinas Medical Center
  • , Dong Hyun JeongAffiliated withDepartment of Computer Science and Information Technology, University of the District of Columbia

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In Traumatic Brain Injury (TBI), elevated Intracranial Pressure (ICP) causes severe brain damages due to hemorrhage and swelling. Monitoring ICP plays an important role in the treatment of TBI patients because ICP is considered a strong predictor of neurological outcome and a potentially amenable method to treat patients. However, it is difficult to predict and measure accurate ICP due to the complex nature of patients’ clinical conditions. ICP monitoring for severe TBI patient is a challenging problem for clinicians because traditionally known ICP monitoring is an invasive procedure by placing a device inside the brain to measure pressure. Therefore, ICP monitoring might have a high infection risk and cause medical complications. In here, an ICP monitoring using texture features is proposed to overcome this limitation. The combination of image processing methods and a decision tree algorithm is utilized to estimate ICP of TBI patients noninvasively. In addition, a visual analytics tool is used to conduct an interactive visual factor analysis and outlier detection.

Key words

Intracranial Pressure Traumatic Brain Injuries Image Processing CART Visual Analytics