Knowledge-based trace abstraction for semantic process mining

Stefania Montani, Manuel Striani, Silvana Quaglini, Anna Cavallini, Giorgio Leonardi

Risultato della ricerca: Capitolo in libro/report/atti di convegnoContributo a conferenzapeer review

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.

Lingua originaleInglese
Titolo della pubblicazione ospiteArtificial Intelligence in Medicine - 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Proceedings
EditorAnnette [surname]ten Teije, Christian Popow, Lucia Sacchi, John H. Holmes
EditoreSpringer Verlag
Pagine267-271
Numero di pagine5
ISBN (stampa)9783319597577
DOI
Stato di pubblicazionePubblicato - 2017
Evento16th Conference on Artificial Intelligence in Medicine, AIME 2017 - Vienna, Austria
Durata: 21 giu 201724 giu 2017

Serie di pubblicazioni

NomeLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10259 LNAI
ISSN (stampa)0302-9743
ISSN (elettronico)1611-3349

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???event.eventtypes.event.conference???16th Conference on Artificial Intelligence in Medicine, AIME 2017
Paese/TerritorioAustria
CittàVienna
Periodo21/06/1724/06/17

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