WEKO3
アイテム
U-survival for prognostic prediction of disease progression and mortality of patients with COVID-19
http://hdl.handle.net/10228/00009065
http://hdl.handle.net/10228/0000906574cbc1fe-56ed-4a3a-9ddb-ee80cd072d4a
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
LaSEINE-2021_001.pdf (2.1 MB)
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| アイテムタイプ | 学術雑誌論文 = Journal Article(1) | |||||||||
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| 公開日 | 2023-02-01 | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||
| 資源タイプ | journal article | |||||||||
| タイトル | ||||||||||
| タイトル | U-survival for prognostic prediction of disease progression and mortality of patients with COVID-19 | |||||||||
| 言語 | en | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| 著者 |
Näppi, Janne J.
× Näppi, Janne J.× Uemura, Tomoki× Watari, Chinatsu× Hironaka, Toru× 神谷, 亨
WEKO
402
× Yoshida, Hiroyuki |
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| 抄録 | ||||||||||
| 内容記述タイプ | Abstract | |||||||||
| 内容記述 | The rapid increase of patients with coronavirus disease 2019 (COVID-19) has introduced major challenges to healthcare services worldwide. Therefore, fast and accurate clinical assessment of COVID-19 progression and mortality is vital for the management of COVID-19 patients. We developed an automated image-based survival prediction model, called U-survival, which combines deep learning of chest CT images with the established survival analysis methodology of an elastic-net Cox survival model. In an evaluation of 383 COVID-19 positive patients from two hospitals, the prognostic bootstrap prediction performance of U-survival was significantly higher (P < 0.0001) than those of existing laboratory and image-based reference predictors both for COVID-19 progression (maximum concordance index: 91.6% [95% confidence interval 91.5, 91.7]) and for mortality (88.7% [88.6, 88.9]), and the separation between the Kaplan–Meier survival curves of patients stratified into low- and high-risk groups was largest for U-survival (P < 3 × 10–14). The results indicate that U-survival can be used to provide automated and objective prognostic predictions for the management of COVID-19 patients. | |||||||||
| 言語 | en | |||||||||
| 書誌情報 |
en : Scientific Reports 巻 11, p. 9263-1-9263-11, 発行日 2021-04-29 |
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| 出版社 | ||||||||||
| 出版者 | Nature Publishing Group | |||||||||
| 言語 | en | |||||||||
| DOI | ||||||||||
| 関連タイプ | isIdenticalTo | |||||||||
| 識別子タイプ | DOI | |||||||||
| 関連識別子 | https://doi.org/10.1038/s41598-021-88591-z | |||||||||
| 日本十進分類法 | ||||||||||
| 主題Scheme | NDC | |||||||||
| 主題 | 493 | |||||||||
| ISSN | ||||||||||
| 収録物識別子タイプ | EISSN | |||||||||
| 収録物識別子 | 2045-2322 | |||||||||
| 著作権関連情報 | ||||||||||
| 権利情報Resource | http://creativecommons.org/licenses/by/4.0/ | |||||||||
| 権利情報 | Copyright (c) The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | |||||||||
| 出版タイプ | ||||||||||
| 出版タイプ | VoR | |||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||
| 査読の有無 | ||||||||||
| 値 | yes | |||||||||
| 連携ID | ||||||||||
| 値 | 10830 | |||||||||
