Name
Abstract | ||
|---|---|---|
Objective: We present a novel machine learning model to accurately predict the blood-analog viscosity during support of a pathological circulation with a rotary ventricular assist device (VAD). The aim is the continuous monitoring of the hematocrit (HCT) of VAD patients with the benefit of a more reliable pump flow estimation and a possible early detection of adverse events, such as bleeding or pu... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TBME.2018.2797424 | IEEE Transactions on Biomedical Engineering |
Keywords | Field | DocType |
Viscosity,Monitoring,Biomedical monitoring,Blood,Testing,Predictive models,Data models | Computer vision,Data modeling,Ventricular assist device,Control theory,Computer science,Remote patient monitoring,Supervised learning,Continuous monitoring,Gaussian process,Artificial intelligence,Test data,Blood pump | Journal |
Volume | Issue | ISSN |
65 | 10 | 0018-9294 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anastasios Petrou | 1 | 0 | 0.34 |
| Menelaos Kanakis | 2 | 0 | 0.34 |
| Stefan Boes | 3 | 1 | 0.82 |
| Panagiotis Pergantis | 4 | 0 | 0.34 |
| Mirko Meboldt | 5 | 0 | 0.34 |
| Marianne Schmid Daners | 6 | 3 | 3.29 |