| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2024-10-18 |
| タイトル |
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タイトル |
Development of Machine Learning-Based Web System for Estimating Pleural Effusion Using Multi-Frequency Bioelectrical Impedance Analyses |
| 言語 |
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言語 |
eng |
| 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 |
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主題 |
impedance |
| キーワード |
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主題Scheme |
Other |
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主題 |
device |
| キーワード |
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主題Scheme |
Other |
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主題 |
estimation system |
| キーワード |
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主題Scheme |
Other |
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主題 |
machine learning |
| 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 |
Nose, Daisuke
松井, 智一
Otsuka, Takuya
松田, 裕貴
Arimura, Tadaaki
安本, 慶一
Sugimoto, Masahiro
Miura, Shin-Ichiro
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| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
Background: Transthoracic impedance values have not been widely used to measure extravascular pulmonary water content due to accuracy and complexity concerns. Our aim was to develop a foundational model for a novel system aiming to non-invasively estimate the intrathoracic condition of heart failure patients. Methods: We employed multi-frequency bioelectrical impedance analysis to simultaneously measure multiple frequencies, collecting electrical, physical, and hematological data from 63 hospitalized heart failure patients and 82 healthy volunteers. Measurements were taken upon admission and after treatment, and longitudinal analysis was conducted. Results: Using a light gradient boosting machine, and a decision tree-based machine learning method, we developed an intrathoracic estimation model based on electrical measurements and clinical findings. Out of the 286 features collected, the model utilized 16 features. Notably, the developed model demonstrated high accuracy in discriminating patients with pleural effusion, achieving an area under the receiver characteristic curves (AUC) of 0.905 (95% CI: 0.870$20130.940, p < 0.0001) in the cross-validation test. The accuracy significantly outperformed the conventional frequency-based method with an AUC of 0.740 (95% CI: 0.688$20130.792, and p < 0.0001). Conclusions: Our findings indicate the potential of machine learning and transthoracic impedance measurements for estimating pleural effusion. By incorporating noninvasive and easily obtainable clinical and laboratory findings, this approach offers an effective means of assessing intrathoracic conditions. |
| 書誌情報 |
en : Journal of Cardiovascular Development and Disease
巻 10,
号 7,
発行日 2023-07-07
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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 |
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収録物識別子 |
2308-3425 |
| 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/jcdd10070291 |
| 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/2308-3425/10/7/291 |
| 権利 |
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権利情報Resource |
https://creativecommons.org/licenses/by/4.0/ |
|
権利情報 |
$00A9 2023 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 |
