title: Permutation entropy analysis of vital signs data for outcome prediction of patients with severe traumatic brain injury.
abstract: Permutation entropy is computationally efficient, robust to outliers, and effective to measure complexity of time series. We used this technique to quantify the complexity of continuous vital signs recorded from patients with traumatic brain injury (TBI). Using permutation entropy calculated from early vital signs (initial 10-20% of patient hospital stay time), we built classifiers to predict in-hospital mortality and mobility, measured by 3-month Extended Glasgow Outcome Score (GOSE). Sixty patients with severe TBI produced a skewed dataset that we evaluated for accuracy, sensitivity and specificity. The overall prediction accuracy achieved 91.67% for mortality, and 76.67% for 3-month GOSE in testing datasets, using the leave-one-out cross validation. We also applied Receiver Operating Characteristic analysis to compare classifiers built from different learning methods. Those results support the applicability of permutation entropy in analyzing the dynamic behavior of TBI vital signs for early prediction of mortality and long-term patient outcomes.
authors.affiliation: Department of Computer Science and Electric Engineering, University of Maryland, Baltimore County, MD 21250, United States. Electronic address: kalpakis@umbc.edu.
authors.forename: Konstantinos
authors.initials: K
authors.lastname: Kalpakis
authors.affiliation: University of Maryland School of Medicine, Baltimore, MD 21201, United States.
authors.forename: Shiming
authors.initials: S
authors.lastname: Yang
authors.affiliation: University of Maryland School of Medicine, Baltimore, MD 21201, United States.
authors.forename: Peter F
authors.initials: PF
authors.lastname: Hu
authors.affiliation: University of Maryland School of Medicine, Baltimore, MD 21201, United States.
authors.forename: Colin F
authors.initials: CF
authors.lastname: Mackenzie
authors.affiliation: University of Maryland School of Medicine, Baltimore, MD 21201, United States.
authors.forename: Lynn G
authors.initials: LG
authors.lastname: Stansbury
authors.affiliation: University of Maryland School of Medicine, Baltimore, MD 21201, United States.
authors.forename: Deborah M
authors.initials: DM
authors.lastname: Stein
authors.affiliation: University of Maryland School of Medicine, Baltimore, MD 21201, United States.
authors.forename: Thomas M
authors.initials: TM
authors.lastname: Scalea
mesh_terms: D001185:Artificial Intelligence; D001930:Brain Injuries / Q000401:mortality / Q000503:physiopathology*; D016208:Databases, Factual; D006801:Humans; D011237:Predictive Value of Tests; D015599:Trauma Severity Indices; D055986:Vital Signs
keywords: Ordinal pattern; Permutation entropy; Prediction; Traumatic brain injury; Vital signs