Abstract
Permutation entropy is computationally efficient, robust to noise, and effective to measure complexity. 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. With early vital signs data, 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 biomedical time series for early prediction of mortality and long-term patient outcomes.
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Kalpakis, K. et al. (2012). Outcome Prediction for Patients with Severe Traumatic Brain Injury Using Permutation Entropy Analysis of Electronic Vital Signs Data. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2012. Lecture Notes in Computer Science(), vol 7376. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31537-4_33
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DOI: https://doi.org/10.1007/978-3-642-31537-4_33
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