Name
Abstract | ||
|---|---|---|
Traditionally, nurse call systems in hospitals are rather simple: patients have a button next to their bed to call a nurse. Which specific nurse is called cannot be controlled, as there is no extra information available. This is different for solutions based on semantic knowledge: if the state of care givers (busy or free), their current position, and for example their skills are known, a system can always choose the best suitable nurse for a call. In this paper we describe such a semantic nurse call system implemented using the EYE reasoner and Notation3 rules. The system is able to perform OWL-RL reasoning. Additionally, we use rules to implement complex decision trees. We compare our solution to an implementation using OWL-DL, the Pellet reasoner, and SPARQL queries. We show that our purely rule-based approach gives promising results. Further improvements will lead to a mature product which will significantly change the organization of modern hospitals. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-21542-6_31 | RULE TECHNOLOGIES: FOUNDATIONS, TOOLS, AND APPLICATIONS |
Keywords | Field | DocType |
Notation3, eHealth, OWL 2 RL | Semantic memory,Ontology,Data mining,Decision tree,Semantic reasoner,Ontology reasoning,Computer science,Semantic Web,SPARQL,eHealth,Database | Conference |
Volume | ISSN | Citations |
9202 | 0302-9743 | 3 |
PageRank | References | Authors |
0.68 | 2 | 11 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dörthe Arndt | 1 | 8 | 2.23 |
| Ben De Meester | 2 | 94 | 18.55 |
| Pieter Bonte | 3 | 19 | 11.80 |
| Jeroen Schaballie | 4 | 11 | 2.55 |
| Jabran Bhatti | 5 | 22 | 4.05 |
| Wim Dereuddre | 6 | 5 | 1.78 |
| Ruben Verborgh | 7 | 630 | 105.49 |
| Femke Ongenae | 8 | 141 | 39.73 |
| Filip De Turck | 9 | 2770 | 297.38 |
| Rik Van de Walle | 10 | 2040 | 238.28 |
| Erik Mannens | 11 | 671 | 99.58 |