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| 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 |
| 顯示於類別: | [嬰幼兒保育系] 期刊論文
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