| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2024-02-07 |
| タイトル |
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タイトル |
Risk of Mortality Prediction Involving Time-Varying Covariates for Patients with Heart Failure Using Deep Learning |
| 言語 |
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|
言語 |
eng |
| キーワード |
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|
主題Scheme |
Other |
|
主題 |
deep learning |
| キーワード |
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|
主題Scheme |
Other |
|
主題 |
heart failure |
| キーワード |
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|
主題Scheme |
Other |
|
主題 |
mortality |
| キーワード |
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|
主題Scheme |
Other |
|
主題 |
risk prediction |
| キーワード |
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|
主題Scheme |
Other |
|
主題 |
time-varying covariates |
| 資源タイプ |
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資源タイプ |
journal article |
| アクセス権 |
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アクセス権 |
open access |
| 著者 |
Nakamura, Keijiro
Zhou, Xue
Sahara, Naohiko
Toyoda, Yasutake
Enomoto, Yoshinari
Hara, Hidehiko
Noro, Mahito
Sugi, Kaoru
Huang, Ming
Moroi, Masao
Nakamura, Masato
Zhu, Xin
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| 抄録 |
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内容記述タイプ |
Abstract |
|
内容記述 |
Heart failure (HF) is challenging public medical and healthcare systems. This study aimed to develop and validate a novel deep learning-based prognostic model to predict the risk of all-cause mortality for patients with HF. We also compared the performance of the proposed model with those of classical deep learning- and traditional statistical-based models. The present study enrolled 730 patients with HF hospitalized at Toho University Ohashi Medical Center between April 2016 and March 2020. A recurrent neural network-based model (RNNSurv) involving time-varying covariates was developed and validated. The proposed RNNSurv showed better prediction performance than those of a deep feed-forward neural network-based model (referred as “DeepSurv”) and a multivariate Cox proportional hazard model in view of discrimination (C-index: 0.839 vs. 0.755 vs. 0.762, respectively), calibration (better fit with a 45-degree line), and ability of risk stratification, especially identifying patients with high risk of mortality. The proposed RNNSurv demonstrated an improved prediction performance in consideration of temporal information from time-varying covariates that could assist clinical decision-making. Additionally, this study found that significant risk and protective factors of mortality were specific to risk levels, highlighting the demand for an individual-specific clinical strategy instead of a uniform one for all patients. |
| 書誌情報 |
en : Diagnostics
巻 12,
号 12,
発行日 2022-11-25
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| 出版者 |
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出版者 |
MDPI |
| ISSN |
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収録物識別子タイプ |
EISSN |
|
収録物識別子 |
2075-4418 |
| 出版者版DOI |
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関連タイプ |
isReplacedBy |
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識別子タイプ |
DOI |
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関連識別子 |
https://doi.org/10.3390/diagnostics12122947 |
| 出版者版URI |
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関連タイプ |
isReplacedBy |
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識別子タイプ |
URI |
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|
関連識別子 |
https://www.mdpi.com/2075-4418/12/12/2947 |
| 権利 |
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|
権利情報Resource |
https://creativecommons.org/licenses/by/4.0/ |
|
権利情報 |
c 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
| 著者版フラグ |
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|
出版タイプ |
NA |
