Name
Abstract | ||
|---|---|---|
The multi-feature joint training and predicting techniques in machine learning can potentially complement and greatly improve the accuracy of traditional blood pressure measurement, resulting in better disease classification and more accurate clinical judgements. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1186/s12859-019-2667-y | BMC bioinformatics |
Keywords | Field | DocType |
Blood pressure prediction,Physiological index data,SVR | Biology,Artificial intelligence,Blood pressure,Bioinformatics,Wearable technology,Machine learning,Continuous measurement,Human health | Journal |
Volume | Issue | ISSN |
20 | 1 | 1471-2105 |
Citations | PageRank | References |
1 | 0.37 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bing Zhang | 1 | 7 | 2.99 |
| Huihui Ren | 2 | 1 | 0.37 |
| Guo-Yan Huang | 3 | 10 | 4.84 |
| Yongqiang Cheng | 4 | 133 | 29.99 |
| Changzhen Hu | 5 | 23 | 14.29 |