Abstract
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.
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Andolina, A., Anselma, L., Piovesan, L., Terenziani, P. (2018). Querying Probabilistic Temporal Constraints for Guideline Interaction Analysis: GLARE’s Approach. In: Simari, G., Fermé, E., Gutiérrez Segura, F., Rodríguez Melquiades, J. (eds) Advances in Artificial Intelligence - IBERAMIA 2018. IBERAMIA 2018. Lecture Notes in Computer Science(), vol 11238. Springer, Cham. https://doi.org/10.1007/978-3-030-03928-8_1
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