Chia Nan University of Pharmacy & Science Institutional Repository:Item 310902800/32512
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    Title: Predicting weaning difficulty for planned extubation patients with an artificial neural network
    Authors: Hsieh, Meng Hsuen
    Hsieh, Meng Ju
    Cheng, Ai-Chin
    Chen, Chin-Ming
    Hsieh, Chia-Chang
    Chao, Chien-Ming
    Lai, Chih-Cheng
    Cheng, Kuo-Chen
    Willy Chou(周偉倪)
    Contributors: Univ Calif Berkeley, Dept Elect Engn & Comp Sci
    Poznan Univ Med Sci, Dept Med
    Chang Jung Christian Univ, Dept Med Sci Ind
    Chi Mei Med Ctr, Dept Internal Med, Sect Resp Care
    Chi Mei Med Ctr, Dept Intens Care Med
    China Med Univ, Childrens Hosp
    Chi Mei Med Ctr, Dept Intens Care Med
    Kaohsiung Vet Gen Hosp, Dept Internal Med
    Chi Mei Med Ctr, Dept Phys Med & Rehabil
    Chia Nan Univ Pharm & Sci, Dept Recreat & Healthcare Management
    Keywords: artificial neural network
    planned extubation
    prediction weaning difficulty
    Date: 2019-10
    Issue Date: 2020-07-29 13:48:01 (UTC+8)
    Publisher: LIPPINCOTT WILLIAMS & WILKINS
    Abstract: This study aims to construct a neural network to predict weaning difficulty among planned extubation patients in intensive care units. This observational cohort study was conducted in eight adult ICUs in a medical center about adult patients experiencing planned extubation. The data of 3602 patients with planned extubation in ICUs of Chi-Mei Medical Center (from Dec. 2009 through Dec. 2011) was used to train and test an artificial neural network (ANN) model. The input features contain 47 clinical risk factors and the outputs are classified into three categories: simple, difficult, and prolonged weaning. A deep ANN model with four hidden layers of 30 neurons each was developed. The accuracy is 0.769 and the area under receiver operating characteristic curve for simple weaning, prolonged weaning, and difficult weaning are 0.910, 0.849, and 0.942 respectively. The results revealed that the ANN model achieved a good performance in prediction the weaning difficulty in planned extubation patients. Such a model will be helpful for predicting ICU patients' successful planned extubation.
    Relation: Medicine, v.98, n.40, e17392
    Appears in Collections:[Dept. of Recreation and Health-Care Management] Periodical Articles

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