Many hospital information systems nowadays record data about the executed medical process instances in the form of traces in an event log. In this paper we present a framework able to convert actions found in the traces into higher level concepts, on the basis of domain knowledge. Abstracted traces are then provided as an input to semantic process mining. The approach has been tested in stroke care, where we show how the abstraction mechanism allows the user to mine process models that are easier to interpret, since unnecessary details are hidden, but key behaviors are clearly visible.

Knowledge-Based Trace Abstraction for Semantic Process Mining

MONTANI, STEFANIA;STRIANI, MANUEL;
2017-01-01

Abstract

Many hospital information systems nowadays record data about the executed medical process instances in the form of traces in an event log. In this paper we present a framework able to convert actions found in the traces into higher level concepts, on the basis of domain knowledge. Abstracted traces are then provided as an input to semantic process mining. The approach has been tested in stroke care, where we show how the abstraction mechanism allows the user to mine process models that are easier to interpret, since unnecessary details are hidden, but key behaviors are clearly visible.
2017
16th Conference on Artificial Intelligence in Medicine - AIME 2017
Vienna, Austria
June 21-24, 2017
Artificial Intelligence in Medicine: 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Vienna, Austria, June 21-24, 2017, Proceedings
Springer International Publishing
10259
267
271
978-3-319-59758-4
http://dx.doi.org/10.1007/978-3-319-59758-4_30
Abstraction, Trace comparison, Process discovery, Stroke management, Ontology
Montani, Stefania; Striani, Manuel; Quaglini, Silvana; Cavallini, Anna; Leonardi, Giorgio
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1642422
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