Querying probabilistic temporal constraints for guideline interaction analysis: GLARE’s approach

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Author(s)
Andolina, A
Anselma, L
Piovesan, L
Terenziani, P
Griffith University Author(s)
Year published
2018
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The treatment of patients affected by multiple diseases (comorbid patients) is one of the main challenges of the modern healthcare, involving the analysis of the interactions of the guidelines for the specific diseases. However, practically speaking, such interactions occur over time. The GLARE project explicitly provides knowledge representation, temporal representation and temporal reasoning methodologies to cope with such a fundamental issue. In this paper, we propose a further improvement, to take into account that, often, the effects of actions have a probabilistic distribution in time, and being able to reason (through ...
View more >The treatment of patients affected by multiple diseases (comorbid patients) is one of the main challenges of the modern healthcare, involving the analysis of the interactions of the guidelines for the specific diseases. However, practically speaking, such interactions occur over time. The GLARE project explicitly provides knowledge representation, temporal representation and temporal reasoning methodologies to cope with such a fundamental issue. In this paper, we propose a further improvement, to take into account that, often, the effects of actions have a probabilistic distribution in time, and being able to reason (through constraint propagation) and to query probabilistic temporal constraints further enhances the support for interaction detection.
View less >
View more >The treatment of patients affected by multiple diseases (comorbid patients) is one of the main challenges of the modern healthcare, involving the analysis of the interactions of the guidelines for the specific diseases. However, practically speaking, such interactions occur over time. The GLARE project explicitly provides knowledge representation, temporal representation and temporal reasoning methodologies to cope with such a fundamental issue. In this paper, we propose a further improvement, to take into account that, often, the effects of actions have a probabilistic distribution in time, and being able to reason (through constraint propagation) and to query probabilistic temporal constraints further enhances the support for interaction detection.
View less >
Conference Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
11238 LNAI
Copyright Statement
© 2018 Springer Berlin/Heidelberg. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher.The original publication is available at www.springerlink.com
Subject
Artificial intelligence