Comput. Biol. Med. - Permutation entropy analysis of vital signs data for outcome prediction of patients with severe traumatic brain injury.

Tópicos

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Resumo

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.

Resumo Limpo

permut entropi comput effici robust outlier effect measur complex time seri use techniqu quantifi complex continu vital sign record patient traumat brain injuri tbi use permut entropi calcul earli vital sign initi patient hospit stay time built classifi predict inhospit mortal mobil measur month extend glasgow outcom score gose sixti patient sever tbi produc skew dataset evalu accuraci sensit specif overal predict accuraci achiev mortal month gose test dataset use leaveoneout cross valid also appli receiv oper characterist analysi compar classifi built differ learn method result support applic permut entropi analyz dynam behavior tbi vital sign earli predict mortal longterm patient outcom

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