title: Outcome Prediction for Patients with Traumatic Brain Injury with Dynamic Features from Intracranial Pressure and Arterial Blood Pressure Signals: A Gaussian Process Approach.
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.
authors.affiliation: Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, UK.
authors.forename: Marco A F
authors.initials: MA
authors.lastname: Pimentel
authors.affiliation: Laboratory for Computational Physiology, Institute for Medical Engineering and Science, E25-505 Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, 02139, USA.
authors.forename: Thomas
authors.initials: T
authors.lastname: Brennan
authors.affiliation: Laboratory for Computational Physiology, Institute for Medical Engineering and Science, E25-505 Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, 02139, USA.
authors.forename: Li-Wei
authors.initials: LW
authors.lastname: Lehman
authors.affiliation: National Neuroscience Institute, Singapore, Singapore.
authors.forename: Nicolas Kon Kam
authors.initials: NK
authors.lastname: King
authors.affiliation: National Neuroscience Institute, Singapore, Singapore.
authors.forename: Beng-Ti
authors.initials: BT
authors.lastname: Ang
authors.affiliation: 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.|Department of Data Analytics, A*STAR, Institute for Infocomm Research, Singapore, Singapore. mfeng@mit.edu.
authors.forename: Mengling
authors.initials: M
authors.lastname: Feng
mesh_terms: D062186:Arterial Pressure / Q000502:physiology*; D001499:Bayes Theorem; D000070642:Brain Injuries, Traumatic / Q000401:mortality / Q000503:physiopathology*; D006801:Humans; D007427:Intracranial Pressure / Q000502:physiology*; D015233:Models, Statistical; D008991:Monitoring, Physiologic; D016011:Normal Distribution; D018458:Persistent Vegetative State / Q000453:epidemiology; D011379:Prognosis; D012372:ROC Curve; D020127:Recovery of Function; D012044:Regression Analysis
keywords: Dynamic features and outcome prediction; Gaussian process; Intracranial pressure