@article{ref1,
title="Feasibility of hidden Markov models for the description of time-varying physiologic state after severe traumatic brain injury",
journal="Critical care medicine",
year="2019",
author="Asgari, Shadnaz and Adams, Hadie and Kasprowicz, Magdalena and Czosnyka, Marek and Smielewski, Peter and Ercole, Ari",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="OBJECTIVES: Continuous assessment of physiology after traumatic brain injury is essential to prevent secondary brain insults. The present work aims at the development of a method for detecting physiologic states associated with the outcome from time-series physiologic measurements using a hidden Markov model. <br><br>DESIGN: Unsupervised clustering of hourly values of intracranial pressure/cerebral perfusion pressure, the compensatory reserve index, and autoregulation status was attempted using a hidden Markov model. A ternary state variable was learned to classify the patient's physiologic state at any point in time into three categories (&quot;good,&quot; &quot;intermediate,&quot; or &quot;poor&quot;) and determined the physiologic parameters associated with each state. SETTING: The proposed hidden Markov model was trained and applied on a large dataset (28,939 hr of data) using a stratified 20-fold cross-validation. PATIENTS: The data were collected from 379 traumatic brain injury patients admitted to Addenbrooke's Hospital, Cambridge between 2002 and 2016. INTERVENTIONS: Retrospective observational analysis. MEASUREMENTS AND MAIN RESULTS: Unsupervised training of the hidden Markov model yielded states characterized by intracranial pressure, cerebral perfusion pressure, compensatory reserve index, and autoregulation status that were physiologically plausible. The resulting classifier retained a dose-dependent prognostic ability. Dynamic analysis suggested that the hidden Markov model was stable over short periods of time consistent with typical timescales for traumatic brain injury pathogenesis. <br><br>CONCLUSIONS: To our knowledge, this is the first application of unsupervised learning to multidimensional time-series traumatic brain injury physiology. We demonstrated that clustering using a hidden Markov model can reduce a complex set of physiologic variables to a simple sequence of clinically plausible time-sensitive physiologic states while retaining prognostic information in a dose-dependent manner. Such states may provide a more natural and parsimonious basis for triggering intervention decisions.<p /> <p>Language: en</p>",
language="en",
issn="0090-3493",
doi="10.1097/CCM.0000000000003966",
url="http://dx.doi.org/10.1097/CCM.0000000000003966"
}