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Deep learning approach for early prediction of COVID-19 mortality using chest X-ray and electronic health records

Title
Deep learning approach for early prediction of COVID-19 mortality using chest X-ray and electronic health records
Authors
BaikSeung MinHongKyung SookParkDong Jin
Ewha Authors
홍경숙백승민
SCOPUS Author ID
홍경숙scopus; 백승민scopusscopus
Issue Date
2023
Journal Title
BMC Bioinformatics
ISSN
1471-2105JCR Link
Citation
BMC Bioinformatics vol. 24, no. 1
Keywords
Chest X-rayCOVID-19Deep learningElectronic health recordPrediction model
Publisher
BioMed Central Ltd
Indexed
SCIE; SCOPUS WOS scopus
Document Type
Article
Abstract
Background: An artificial-intelligence (AI) model for predicting the prognosis or mortality of coronavirus disease 2019 (COVID-19) patients will allow efficient allocation of limited medical resources. We developed an early mortality prediction ensemble model for COVID-19 using AI models with initial chest X-ray and electronic health record (EHR) data. Results: We used convolutional neural network (CNN) models (Inception-ResNet-V2 and EfficientNet) for chest X-ray analysis and multilayer perceptron (MLP), Extreme Gradient Boosting (XGBoost), and random forest (RF) models for EHR data analysis. The Gradient-weighted Class Activation Mapping and Shapley Additive Explanations (SHAP) methods were used to determine the effects of these features on COVID-19. We developed an ensemble model (Area under the receiver operating characteristic curve of 0.8698) using a soft voting method with weight differences for CNN, XGBoost, MLP, and RF models. To resolve the data imbalance, we conducted F1-score optimization by adjusting the cutoff values to optimize the model performance (F1 score of 0.77). Conclusions: Our study is meaningful in that we developed an early mortality prediction model using only the initial chest X-ray and EHR data of COVID-19 patients. Early prediction of the clinical courses of patients is helpful for not only treatment but also bed management. Our results confirmed the performance improvement of the ensemble model achieved by combining AI models. Through the SHAP method, laboratory tests that indicate the factors affecting COVID-19 mortality were discovered, highlighting the importance of these tests in managing COVID-19 patients. © 2023, The Author(s).
DOI
10.1186/s12859-023-05321-0
Appears in Collections:
의과대학 > 의학과 > Journal papers
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