Name
Title | ||
|---|---|---|
Inspiratory respiratory mechanics estimation by using expiratory data for reverse-triggered breathing cycles. |
Abstract | ||
|---|---|---|
This study shows that when conventional methods fail, typically ignored expiratory data may be able to provide clinicians with the information needed about patient condition to guide MV therapy. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.cmpb.2019.105184 | Computer Methods and Programs in Biomedicine |
Keywords | Field | DocType |
Mechanical ventilation,Model-based methods,Expiration,Intensive care,Time constant | Computer vision,Lung,Internal medicine,Computer science,Cardiology,Airway resistance,Breathing,Artificial intelligence,Expiration,Intensive care,Respiratory physiology,Elastance | Journal |
Volume | ISSN | Citations |
186 | 0169-2607 | 0 |
PageRank | References | Authors |
0.34 | 0 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| S L Howe | 1 | 0 | 0.68 |
| J. G. Chase | 2 | 33 | 7.52 |
| Daniel P. Redmond | 3 | 0 | 2.37 |
| S E Morton | 4 | 0 | 0.34 |
| K T Kim | 5 | 0 | 0.34 |
| Christopher G Pretty | 6 | 58 | 19.03 |
| G.M. Shaw | 7 | 20 | 3.49 |
| Merryn H. Tawhai | 8 | 24 | 5.38 |
| Thomas Desaive | 9 | 34 | 14.02 |