A Knowledge-based Approach to Handling Exceptions inWorkflow Systems
Computer Supported Cooperative Work
Exception Handling in Workflow Systems
Applied Intelligence
Error Handling in Process Support Systems
Advances in Exception Handling Techniques (the book grow out of a ECOOP 2000 workshop)
AGENT WORK: a workflow system supporting rule-based workflow adaptation
Data & Knowledge Engineering
Managing exceptions in the medical workflow systems
Proceedings of the 28th international conference on Software engineering
A methodology for eliciting and modeling exceptions
Journal of Biomedical Informatics
A goal-oriented framework for specifying clinical guidelines and handling medical errors
Journal of Biomedical Informatics
BPM'07 Proceedings of the 2007 international conference on Business process management
KR4HC'11 Proceedings of the 3rd international conference on Knowledge Representation for Health-Care
Artificial Intelligence in Medicine
Knowledge-Driven adaptive execution of care pathways based on continuous planning techniques
BPM' 2012 Proceedings of the 2012 international conference on Process Support and Knowledge Representation in Health Care
Knowledge-Driven adaptive execution of care pathways based on continuous planning techniques
BPM' 2012 Proceedings of the 2012 international conference on Process Support and Knowledge Representation in Health Care
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This work presents an approach for representing and managing medical exceptions that may occur during the execution of a patient-centered care pathway. Personalized care pathways are generated automatically by means of a knowledge-driven planning process over a temporal hierarchical task network (HTN), which encodes an evidence-based clinical guideline. The exceptional situations specified in this guideline as well as the recommendations for their management are represented by knowledge-based rules in the task network model. However these rules, which encode the exceptional flow of the guideline, are represented separately from the normal flow in order to not obscure the modelling. Moreover, we propose the use of medical concepts from a standard terminology (UMLS) for the formal representation of these rules. This fact promotes interoperability, knowledge sharing and precision aspects. Finally, a therapy planning system with capabilities for exception detection, analysis and adaptation has been developed. As a result, the proposal, which is evaluated with oncology care plans, seems to be an adequate exception recovery mechanism maintaining guideline adherence.