Deep Learning–Based Time-to-Death Prediction Model for COVID-19 Patients Using Clinical Data and Chest Radiographs.
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 pat...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 1; pp. 178 - 189 |
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| Autores principales: | , , , , , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=162233247&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 162233247 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2023 vid: 36 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 162233247 158407559 162233247 162233247 10.1007/s10278-022-00691-y 162233247 ppf: 178 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep Learning–Based Time-to-Death Prediction Model for COVID-19 Patients Using Clinical Data and Chest Radiographs. aug: au: Matsumoto, Toshimasa Walston, Shannon Leigh Walston, Michael Kabata, Daijiro Miki, Yukio Shiba, Masatsugu Ueda, Daiju affil: Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, 545-8585, Osaka, Japan sug: subj: COVID-19 Prognosis Death Risk Factors Risk Assessment Methods Radiography, Thoracic Image Processing, Computer Assisted Methods Deep Learning Prediction Models Evaluation Human Male Female Adolescence Adult Middle Age Aged Aged, 80 and Over Retrospective Design Record Review Artificial Intelligence Cox Proportional Hazards Model Neural Networks (Computer) Time Factors Adolescent: 13-18 years Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: 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. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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