Name
Abstract | ||
|---|---|---|
Medical process mining is gaining much attention in recent years, but the available mining algorithms can hardly cope with medical application peculiarities, that require to properly contextualize process patterns. Indeed, most approaches lose the connection between a mined pattern and the relevant portion of the input event log, and can have a limited precision, i.e., they can mine incorrect paths, never appearing in the input log traces. These issues can be very harmful in medical applications, where it is vital that mining results are reliable as much as possible, and properly reference the contextual information, in order to facilitate the work of physicians and hospital managers in guaranteeing the highest quality of service to patients. In this paper, we propose a novel approach to medical process mining that operates in a context-aware fashion. We show on a set of critical examples how our algorithm is able to cope with all the issues sketched above. In the future, we plan to test the approach on a real-world medical dataset, and to extend the framework in order to support efficient and flexible trace querying as well. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.20368/1971-8829/1453 | JOURNAL OF E-LEARNING AND KNOWLEDGE SOCIETY |
Keywords | Field | DocType |
Process Mining,Medical Processes,Context | Data science,Contextual information,Computer science,Quality of service,Process patterns,Process mining | Conference |
Volume | Issue | ISSN |
14 | 1 | 1826-6223 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luca Canensi | 1 | 1 | 1.70 |
| Giorgio Leonardi | 2 | 179 | 20.36 |
| Stefania Montani | 3 | 901 | 81.42 |
| Paolo Terenziani | 4 | 924 | 112.83 |