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    Please use this identifier to cite or link to this item: https://ir.cnu.edu.tw/handle/310902800/34393


    標題: An artificial neural network model to predict the mortality of COVID-19 patients using routine blood samples at the time of hospital admission Development and validation study
    作者: Lin, Ju-Kuo
    Chien, Tsair-Wei
    Wang, Lin-Yen
    Chou, Willy
    貢獻者: Chi Mei Med Ctr, Dept Ophthalmol
    Chung Hwa Univ Med Technol, Dept Optometry
    Chi Mei Med Ctr, Dept Med Res
    Chi Mei Med Ctr, Dept Pediat
    Chia Nan Univ Pharm & Sci, Dept Childhood Educ & Nursery
    Kaohsiung Med Univ, Coll Med, Sch Med
    Chung San Med Univ Hosp, Dept Phys Med & Rehabil
    Chi Mei Med Ctr, Dept Phys Med & Rehabil
    關鍵字: app
    artificial neural network
    convolutional neural network
    Google Maps
    predict the mortality of COVID-19 patients
    receiver operating characteristic curve
    日期: 2021
    上傳時間: 2023-11-11 11:49:24 (UTC+8)
    出版者: LIPPINCOTT WILLIAMS & WILKINS
    摘要: Background: In a pandemic situation (e.g., COVID-19), the most important issue is to select patients at risk of high mortality at an early stage and to provide appropriate treatments. However, a few studies applied the model to predict in-hospital mortality using routine blood samples at the time of hospital admission. This study aimed to develop an app, name predict the mortality of COVID-19 patients (PMCP) app, to predict the mortality of COVID-19 patients at hospital-admission time. Methods: We downloaded patient records from 2 studies, including 361 COVID-19 patients in Wuhan, China, and 106 COVID-19 patients in 3 Korean medical institutions. A total of 30 feature variables were retrieved, consisting of 28 blood biomarkers and 2 demographic variables (i.e., age and gender) of patients. Two models, namely, artificial neural network (ANN) and convolutional neural network (CNN), were compared with each other across 2 scenarios using 1. raw laboratory versus normalized data and 2. training vs testing datasets (n = 361 and n = 106/361 approximately equal to 30%) to verify the model performance (e.g., sensitivity [SENS], specificity [SPEC], and area under the receiver operating characteristic curve [AUC]). An app for predicting the mortality of COVID-19 patients was developed using the model's estimated parameters for the prediction and classification of PMCP at an earlier stage. Feature variables and prediction results were visualized using the forest plot and category probability curves shown on Google Maps. Results: We observed that 1. the normalized dataset gains a relatively higher AUC(>0.9) when compared to that(<0.9) in the raw-laboratory dataset based on training data, 2. the normalized dataset in ANN yielded a high AUC of 0.96 that that(=0.91) in CNN based on testing data, and 3. a ready and available app, where anyone can access the model to predict mortality, for PMCP was developed in this study. Conclusions: Our new PMCP app with ANN model accurately predicts the mortality probability for COVID-19 patients. It is publicly available and aims to help health care providers fight COVID-19 and improve patients' classifications against treatment risk.
    關聯: MEDICINE, v.100, n.28
    Appears in Collections:[嬰幼兒保育系] 期刊論文

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