Name
Title | ||
|---|---|---|
An annotated association mining approach for extracting and visualizing interesting clinical events |
Abstract | ||
|---|---|---|
•A plug-in for standalone association mining to generate temporal clinical rules.•Unwanted rules are discarded during rule generation, and not a-posteriori.•A back-end allows domain experts to annotate the clinical feature sequence.•A graphical interface allows the end-user to create and unfold clinical events.•Clinicians can navigate hospital scenarios and students can use the system to train. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.ijmedinf.2020.104366 | International Journal of Medical Informatics |
Keywords | DocType | Volume |
Association rule mining,Clinical event recognition,Sequence mining | Journal | 148 |
ISSN | Citations | PageRank |
1386-5056 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aashara Shrestha | 1 | 0 | 0.34 |
| Dimitrios Zikos | 2 | 0 | 0.34 |
| Leonidas Fegaras | 3 | 0 | 0.34 |