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    請使用永久網址來引用或連結此文件: https://ir.cnu.edu.tw/handle/310902800/34430


    標題: Predicting the 14-Day Hospital Readmission of Patients with Pneumonia Using Artificial Neural Networks (ANN)
    作者: Tey, Shu-Farn
    Liu, Chung-Feng
    Chien, Tsair-Wei
    Hsu, Chin-Wei
    Chan, Kun-Chen
    Chen, Chia-Jung
    Cheng, Tain-Junn
    Wu, Wen-Shiann
    貢獻者: Chi Mei Med Ctr, Pulm Med
    Chi Mei Med Ctr, Dept Med Res
    Chi Mei Med Ctr, Dept Pharm
    Chi Mei Med Ctr, Div Clin Pathol
    Chi Mei Med Ctr, Dept Informat Syst
    Chi Mei Med Ctr, Dept Neurol
    Chi Mei Med Ctr, Dept Occupat Med
    Chi Mei Med Ctr, Div Cardiovasc Med
    Chia Nan Univ Pharm & Sci, Dept Pharm
    關鍵字: unplanned patient readmission
    artificial neural network
    convolutional neural network
    nurse
    Microsoft Excel
    receiver operating characteristic curve
    日期: 2021
    上傳時間: 2023-11-11 11:52:21 (UTC+8)
    出版者: MDPI
    摘要: Unplanned patient readmission (UPRA) is frequent and costly in healthcare settings. No indicators during hospitalization have been suggested to clinicians as useful for identifying patients at high risk of UPRA. This study aimed to create a prediction model for the early detection of 14-day UPRA of patients with pneumonia. We downloaded the data of patients with pneumonia as the primary disease (e.g., ICD-10:J12*-J18*) at three hospitals in Taiwan from 2016 to 2018. A total of 21,892 cases (1208 (6%) for UPRA) were collected. Two models, namely, artificial neural network (ANN) and convolutional neural network (CNN), were compared using the training (n = 15,324; approximately equal to 70%) and test (n = 6568; approximately equal to 30%) sets to verify the model accuracy. An app was developed for the prediction and classification of UPRA. We observed that (i) the 17 feature variables extracted in this study yielded a high area under the receiver operating characteristic curve of 0.75 using the ANN model and that (ii) the ANN exhibited better AUC (0.73) than the CNN (0.50), and (iii) a ready and available app for predicting UHA was developed. The app could help clinicians predict UPRA of patients with pneumonia at an early stage and enable them to formulate preparedness plans near or after patient discharge from hospitalization.
    關聯: INT J ENV RES PUB HE, v.18, n.10
    顯示於類別:[藥學系(所)] 期刊論文

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