Predictability of intracranial pressure level in traumatic brain injury: features extraction, statistical analysis and machine learning-based evaluation

Authors:
Wenan Chen;Charles H. Cockrell;Kevin Ward;Kayvan Najarian
Affiliations:
Virginia Commonwealth University, Reanimation Engineering Science VCURES Centre, Department of Computer Science, Virginia Commonwealth University, Richmond 23298, VA, USA;Virginia Commonwealth University, Reanimation Engineering Science VCURES Centre, Department of Radiology, Virginia Commonwealth University, Richmond 23298, VA, USA;Virginia Commonwealth University, Reanimation Engineering Science VCURES Centre, Department of Emergency Medicine, Virginia Commonwealth University, Richmond 23298, VA, USA;Virginia Commonwealth University, Reanimation Engineering Science VCURES Centre, Department of Computer Science, Virginia Commonwealth University, Richmond 23298, VA, USA
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2013

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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.