A machine learning approach to assess magnitude of asynchrony breathing
Background: Conventional patient-ventilator interaction (PVI) assessment involves manual asynchronous index (AI) computation and incapable to provide in-depth information of the severity of asynchrony breathing (AB) during mechanical ventilation (MV). In this study, a novel convolutional autoencod...
保存先:
| 主要な著者: | , , , , |
|---|---|
| フォーマット: | 論文 |
| 言語: | English English |
| 出版事項: |
Elsevier Ltd.
2021
|
| 主題: | |
| オンライン･アクセス: | http://irep.iium.edu.my/89103/7/89103_A%20machine%20learning%20approach%20to%20assess%20magnitude%20of%20asynchrony%20breathing%20-%20Loo.pdf http://irep.iium.edu.my/89103/8/89103_Scopus%20-%20A%20machine%20learning%20approach%20to.pdf http://irep.iium.edu.my/89103/ https://www.sciencedirect.com/science/article/abs/pii/S1746809421001026?via%3Dihub |
| タグ: |
タグ追加
タグなし, このレコードへの初めてのタグを付けませんか!
|
インターネット
http://irep.iium.edu.my/89103/7/89103_A%20machine%20learning%20approach%20to%20assess%20magnitude%20of%20asynchrony%20breathing%20-%20Loo.pdfhttp://irep.iium.edu.my/89103/8/89103_Scopus%20-%20A%20machine%20learning%20approach%20to.pdf
http://irep.iium.edu.my/89103/
https://www.sciencedirect.com/science/article/abs/pii/S1746809421001026?via%3Dihub