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Expert-based probabilistic modeling of workflows in context of surgical interventions

: Philipp, P.; Beyerer, Jürgen; Fischer, Yvonne

Postprint urn:nbn:de:0011-n-4614061 (833 KByte PDF)
MD5 Fingerprint: 1954239e7ab29b5ee6de7c9b04a1dc64
Erstellt am: 10.8.2017

Schaefer, K. ; Institute of Electrical and Electronics Engineers -IEEE-; IEEE Communications Society; IEEE Systems, Man, and Cybernetics Society:
IEEE Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2017 : March 27-31, 2017, Savannah, GA, USA
Piscataway, NJ: IEEE, 2017
ISBN: 978-1-5090-6380-2
ISBN: 978-1-5090-6381-9 (Print)
Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA) <2017, Savannah/Ga.>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
surgical workflow; dynamic Bayesian networks; Unified Modeling Language; assistance; planning

To provide assistance functions in context of surgical interventions, the use of medical workflows plays an important role. Workflow models can be used to assess the progress of an on-going surgery, enabling tailored (i.e., context sensitive) support for the medical practitioner. Subsequently, this provides opportunities to prevent malpractices, to enhance the patient's outcome and to preserve a high level of satisfaction. In this work, we propose a framework which enables a formalization of medical workflows. It is driven by a dialog of medical as well as technical experts and is based on the Unified Modeling Language (UML). An easy comprehensible UML activity serves as a starting point for the automatic generation of more complex models that can be used for the actual estimation of the progress of a surgical intervention. In this work, we present translation rules, which allow to transfer a given UML activity into a Dynamic Bayesian Network (DBN). The methods are presented for the application example of a cholecystectomy (surgical removal of the gallbladder).