Optimal machine learning methods for prediction of high-flow nasal cannula outcomes using image features from electrical impedance tomography
Author: | Lin Yang, Zhe Li, Meng Dai, Feng Fu, Knut MöllerORCiDGND, Yuan Gao, Zhanqi ZhaoORCiDGND |
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URN: | https://urn:nbn:de:bsz:fn1-opus4-101782 |
DOI: | https://doi.org/10.1016/j.cmpb.2023.107613 |
ISSN: | 1872-7565 |
Parent Title (English): | Computer Methods and Programs in Biomedicine |
Document Type: | Article (peer-reviewed) |
Language: | English |
Year of Completion: | 2023 |
Release Date: | 2023/12/19 |
Tag: | Early prediction; Electrical impedance tomography; High-flow nasal cannula; Image features; Machine learning |
Volume: | 238.2023 |
Issue: | August |
Article Number: | 107613 |
Page Number: | 10 |
Open-Access-Status: | Open Access |
Hybrid | |
Licence (German): | ![]() |