Predictability of intracranial pressure level in traumatic brain injury: features extraction, statistical analysis and machine learning-based evaluation
by Wenan Chen; Charles H. Cockrell; Kevin Ward; Kayvan Najarian
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 8, No. 4, 2013

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.

Online publication date: Mon, 20-Oct-2014

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