title: Predictability of intracranial pressure level in traumatic brain injury: features extraction, statistical analysis and machine learning-based evaluation.
abstract: This paper attempts to predict Intracranial Pressure (ICP) based on features extracted from non-invasively collected patient data. These features include midline shift measurement and textural features extracted from Computed axial Tomography (CT) images. A statistical analysis is performed to examine the relationship between ICP and midline shift. Machine learning is also applied to estimate ICP levels with a two-stage feature selection scheme. To avoid overfitting, all feature selections and parameter selections are performed using a nested 10-fold cross validation within the training data. The classification results demonstrate the effectiveness of the proposed method in ICP prediction.
authors.affiliation: Reanimation Engineering Science (VCURES) Centre, Department of Computer Science, Virginia Commonwealth University, Richmond 23298, VA, USA. chenw6@vcu.edu
authors.forename: Wenan
authors.initials: W
authors.lastname: Chen
authors.affiliation: Reanimation Engineering Science (VCURES) Centre, Department of Radiology, Virginia Commonwealth University, Richmond 23298, VA, USA.
authors.forename: Charles H
authors.initials: CH
authors.lastname: Cockrell
authors.affiliation: Reanimation Engineering Science (VCURES) Centre, Department of Emergency Medicine, Virginia Commonwealth University, Richmond 23298, VA, USA.
authors.forename: Kevin
authors.initials: K
authors.lastname: Ward
authors.affiliation: Reanimation Engineering Science (VCURES) Centre, Department of Computer Science, Virginia Commonwealth University, Richmond 23298, VA, USA.
authors.forename: Kayvan
authors.initials: K
authors.lastname: Najarian
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000503:physiopathology; D001930:Brain Injuries / Q000503:physiopathology*; D003627:Data Interpretation, Statistical; D006801:Humans; D007427:Intracranial Pressure