Name
Title | ||
|---|---|---|
Accounting for Label Uncertainty in Machine Learning for Detection of Acute Respiratory Distress Syndrome. |
Abstract | ||
|---|---|---|
When training a machine learning algorithm for a supervised-learning task in some clinical applications, uncertainty in the correct labels of some patients may adversely affect the performance of the algorithm. For example, even clinical experts may have less confidence when assigning a medical diagnosis to some patients because of ambiguity in the patient's case or imperfect reliability of the di... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/JBHI.2018.2810820 | IEEE Journal of Biomedical and Health Informatics |
Keywords | Field | DocType |
Supervised learning,Uncertainty,Medical conditions,Support vector machines,Machine learning algorithms,Electronic medical records,Medical diagnosis,Sampling methods | ARDS,Data modeling,Computer science,Support vector machine,Acute respiratory distress,Artificial intelligence,Overfitting,Ambiguity,Medical diagnosis,Machine learning | Journal |
Volume | Issue | ISSN |
23 | 1 | 2168-2194 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Narathip Reamaroon | 1 | 7 | 2.09 |
| Michael W Sjoding | 2 | 1 | 2.06 |
| Kaiwen Lin | 3 | 0 | 0.34 |
| Theodore J. Iwashyna | 4 | 12 | 1.68 |
| Kayvan Najarian | 5 | 262 | 59.53 |