Deep Learning–Based Time-to-Death Prediction Model for COVID-19 Patients Using Clinical Data and Chest Radiographs
Open accessAbstract
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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The COVID-19 pandemic increased the number of COVID-19-related studies from Japan, with a significant increase in such research focusing on AI.
“Furthermore, owing to the coronavirus disease 2019 (COVID-19) pandemic, the number of COVID-19-related studies from Japan has increased [54–61], with a significant increase in such research focusing on AI [62–64].”
From Fairness of artificial intelligence in healthcare: review and recommendations (2024) · cites this paper as [63]
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Fairness of artificial intelligence in healthcare: review and recommendations
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- Authors
- Matsumoto, T., Walston, S. L., Walston, M., Kabata, D., Miki, Y., Shiba, M., & Ueda, D.
- Affiliations
- Osaka Metropolitan University (JP)
- Center for Health Science Innovation, Osaka Metropolitan University (JP)
- Corresponding author
- Ueda, Daiju
- ORCID
- Venue
- Journal of Digital Imaging
- Published
- 2022-08-08
- Volume
- 36
- Issue
- 1
- Pages
- 178-188
- ISSN
- 0897-1889 (ISSN-L)
- 1618-727X
- PMC
- PMC9360661
- OpenAlex
- W4292283910
- Cited by
- 15
- References
- 56
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 4 · 2023: 5 · 2022: 1
- FWCI
- 1.99
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Imaging Informatics in Medicine (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Japan Society for the Promotion of Science (17K10415)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Keywords
- COVID-19 pandemic, mortality prediction, chest radiograph, DeepSurv, convolutional neural networks, Cox proportional hazards model, time-to-event prediction, clinical data
- MeSH terms
- Deep Learning, COVID-19, Artificial Intelligence, Humans, Radiography, Retrospective Studies
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