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Acta Neurochir Suppl. 2016;122:85-91. doi: 10.1007/978-3-319-22533-3_17.

Outcome Prediction for Patients with Traumatic Brain Injury with Dynamic Features from Intracranial Pressure and Arterial Blood Pressure Signals: A Gaussian Process Approach.

Author information

1
Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, UK.
2
Laboratory for Computational Physiology, Institute for Medical Engineering and Science, E25-505 Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, 02139, USA.
3
National Neuroscience Institute, Singapore, Singapore.
4
Laboratory for Computational Physiology, Institute for Medical Engineering and Science, E25-505 Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, 02139, USA. mfeng@mit.edu.
5
Department of Data Analytics, A*STAR, Institute for Infocomm Research, Singapore, Singapore. mfeng@mit.edu.

Abstract

Previous work has been demonstrated that tracking features describing the dynamic and time-varying patterns in brain monitoring signals provide additional predictive information beyond that derived from static features based on snapshot measurements. To achieve more accurate predictions of outcomes of patients with traumatic brain injury (TBI), we proposed a statistical framework to extract dynamic features from brain monitoring signals based on the framework of Gaussian processes (GPs). GPs provide an explicit probabilistic, nonparametric Bayesian approach to metric regression problems. This not only provides probabilistic predictions, but also gives the ability to cope with missing data and infer model parameters such as those that control the function's shape, noise level and dynamics of the signal. Through experimental evaluation, we have demonstrated that dynamic features extracted from GPs provide additional predictive information in addition to the features based on the pressure reactivity index (PRx). Significant improvements in patient outcome prediction were achieved by combining GP-based and PRx-based dynamic features. In particular, compared with the a baseline PRx-based model, the combined model achieved over 30 % improvement in prediction accuracy and sensitivity and over 20 % improvement in specificity and the area under the receiver operating characteristic curve.

KEYWORDS:

Dynamic features and outcome prediction; Gaussian process; Intracranial pressure

PMID:
27165883
PMCID:
PMC5484054
DOI:
10.1007/978-3-319-22533-3_17
[Indexed for MEDLINE]
Free PMC Article

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