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Published in: Journal of Digital Imaging 1/2023

08-08-2022 | Artificial Intelligence | Original Paper

Deep Learning–Based Time-to-Death Prediction Model for COVID-19 Patients Using Clinical Data and Chest Radiographs

Authors: Toshimasa Matsumoto, Shannon Leigh Walston, Michael Walston, Daijiro Kabata, Yukio Miki, Masatsugu Shiba, Daiju Ueda

Published in: Journal of Imaging Informatics in Medicine | Issue 1/2023

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Abstract

Accurate estimation of mortality and time to death at admission for COVID-19 patients is important and several deep learning models have been created for this task. However, there are currently no prognostic models which use end-to-end deep learning to predict time to event for admitted COVID-19 patients using chest radiographs and clinical data. We retrospectively implemented a new artificial intelligence model combining DeepSurv (a multiple-perceptron implementation of the Cox proportional hazards model) and a convolutional neural network (CNN) using 1356 COVID-19 inpatients. For comparison, we also prepared DeepSurv only with clinical data, DeepSurv only with images (CNNSurv), and Cox proportional hazards models. Clinical data and chest radiographs at admission were used to estimate patient outcome (death or discharge) and duration to the outcome. The Harrel’s concordance index (c-index) of the DeepSurv with CNN model was 0.82 (0.75–0.88) and this was significantly higher than the DeepSurv only with clinical data model (c-index = 0.77 (0.69–0.84), p = 0.011), CNNSurv (c-index = 0.70 (0.63–0.79), p = 0.001), and the Cox proportional hazards model (c-index = 0.71 (0.63–0.79), p = 0.001). These results suggest that the time-to-event prognosis model became more accurate when chest radiographs and clinical data were used together.
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Metadata
Title
Deep Learning–Based Time-to-Death Prediction Model for COVID-19 Patients Using Clinical Data and Chest Radiographs
Authors
Toshimasa Matsumoto
Shannon Leigh Walston
Michael Walston
Daijiro Kabata
Yukio Miki
Masatsugu Shiba
Daiju Ueda
Publication date
08-08-2022
Publisher
Springer International Publishing
Published in
Journal of Imaging Informatics in Medicine / Issue 1/2023
Print ISSN: 2948-2925
Electronic ISSN: 2948-2933
DOI
https://doi.org/10.1007/s10278-022-00691-y

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