Name
Title | ||
|---|---|---|
A predictive analytics framework for identifying patients at risk of developing multiple medical complications caused by chronic diseases. |
Abstract | ||
|---|---|---|
•Patients with chronic diseases are often at risk for multiple correlated complications.•Single-task learning predicts these complications but ignores their correlations.•We use single- and multi-task learning with different predictive models.•We compare prediction performance of hypertrophic cardiomyopathy complications.•We show multi-task learning implemented by logistic regression has the best performance. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.artmed.2019.101750 | Artificial Intelligence in Medicine |
Keywords | Field | DocType |
Predictive analytics,Chronic disease,Artificial neural networks,Multi-Task learning,Regression. | Data mining,Decision tree,Predictive analytics,Computer science,Support vector machine,Correlation,Medical record,Artificial intelligence,Artificial neural network,Chronic disease,Logistic regression,Machine learning | Journal |
Volume | ISSN | Citations |
101 | 0933-3657 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amir Talaei-Khoei | 1 | 52 | 15.63 |
| Madjid Tavana | 2 | 865 | 77.39 |
| James M. Wilson | 3 | 3 | 1.09 |