Name
Abstract | ||
|---|---|---|
Intensive Care is one of the most critical areas ofmedicine. Its multidisciplinary nature makes it a very wide area, requiring all types of healthcare professionals. Given the criticalenvironment of intensive care units, it becomes evident the need touse technology of decision support systems to improve healthcareservices and Intensive Care Units management. By discovering thecommon characteristics of the admitted patients it is possible toimprove these outcomes. In this study clustering techniques wereapplied to data collected from admitted patients in Intensive CareUnit. The best results presented a Silhouette of 1, with a distance tocentroids of 6.2e-17 and a Davies-Bouldin index of -0.652. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/CBI.2016.48 | 2016 IEEE 18th Conference on Business Informatics (CBI) |
Keywords | DocType | Volume |
data mining,clustering,intensive care,admissions,INTCare | Journal | 02 |
Issue | ISSN | ISBN |
1 | 2378-1963 | 978-1-5090-3232-7 |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ana Ribeiro | 1 | 0 | 0.34 |
| Filipe Portela | 2 | 177 | 44.10 |
| Manuel Filipe Santos | 3 | 360 | 68.91 |
| José Machado | 4 | 83 | 32.46 |
| António Abelha | 5 | 243 | 57.30 |
| Fernando Rua | 6 | 78 | 15.32 |