Name
Title | ||
|---|---|---|
A Deep Learning Approach to Predict Diabetes’ Cardiovascular Complications From Administrative Claims |
Abstract | ||
|---|---|---|
People with diabetes require lifelong access to healthcare services to delay the onset of complications. Their disease management processes generate great volumes of data across several domains, from clinical to administrative. Difficulties in accessing and processing these data hinder their secondary use in an institutional setting, even for highly desirable applications, such as the prediction o... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/JBHI.2021.3065756 | IEEE Journal of Biomedical and Health Informatics |
Keywords | DocType | Volume |
Diabetes,Medical services,Deep learning,Indexes,Data models,Cardiovascular diseases,Statistics | Journal | 25 |
Issue | ISSN | Citations |
9 | 2168-2194 | 1 |
PageRank | References | Authors |
0.39 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Enrico Longato | 1 | 1 | 2.41 |
| Gian Paolo Fadini | 2 | 1 | 0.39 |
| Giovanni Sparacino | 3 | 276 | 52.52 |
| Angelo Avogaro | 4 | 1 | 0.39 |
| Lara Tramontan | 5 | 1 | 0.72 |
| Barbara Di Camillo | 6 | 105 | 18.19 |