Optimal machine learning methods for prediction of high-flow nasal cannula outcomes using image features from electrical impedance tomography
Download full text files
Additional Services
Statistics
| Document Type: | Article (peer-reviewed) |
|---|---|
| Author: | Lin Yang, Zhe Li, Meng Dai, Feng Fu, Knut MöllerORCiDGND, Yuan Gao, Zhanqi ZhaoORCiDGND |
| 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 |
| 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): |  Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International |