title: Intracranial hypertension prediction using extremely randomized decision trees.
abstract: Intracranial pressure (ICP) elevation (intracranial hypertension, IH) in neurocritical care is typically treated in a reactive fashion; it is only delivered after bedside clinicians notice prolonged ICP elevation. A proactive solution is desirable to improve the treatment of intracranial hypertension. Several studies have shown that the waveform morphology of the intracranial pressure pulse holds predictors about future intracranial hypertension and could therefore be used to alert the bedside clinician of a likely occurrence of the elevation in the immediate future. In this paper, a computational framework is proposed to predict prolonged intracranial hypertension based on morphological waveform features computed from the ICP. A key contribution of this work is to exploit an ensemble classifier method based on extremely randomized decision trees (Extra-Trees). Experiments on a representative set of 30 patients admitted for various intracranial pressure related conditions demonstrate the effectiveness of the predicting framework on ICP pulses acquired under clinical conditions and the superior results of the proposed approach in comparison to linear and AdaBoost classifiers.
authors.affiliation: Neurosurgery Neural Systems and Dynamics Laboratory, Department of Neurosurgery, Geffen School of Medicine, University of California, Los Angeles, USA. fabien.scalzo@gmail.com
authors.forename: Fabien
authors.initials: F
authors.lastname: Scalzo
authors.forename: Robert
authors.initials: R
authors.lastname: Hamilton
authors.forename: Shadnaz
authors.initials: S
authors.lastname: Asgari
authors.forename: Sunghan
authors.initials: S
authors.lastname: Kim
authors.forename: Xiao
authors.initials: X
authors.lastname: Hu
mesh_terms: D000465:Algorithms; D003663:Decision Trees; D006801:Humans; D019586:Intracranial Hypertension / Q000175:diagnosis* / Q000503:physiopathology; D007427:Intracranial Pressure; D016014:Linear Models; D011897:Random Allocation; D013997:Time Factors