Clinical practice guidelines are assuming a major role in the medical area, to provide physicians with evidence-based recommendations for the treatment of single pathologies. The treatment of comorbid patients (i.e., patients affected by multiple diseases) is one of the main challenges for the modern healthcare. It requires the development of new methodologies, supporting physicians in the treatment of interactions between guidelines. Several Artificial Intelligence approaches have started to face such a challenging problem. However, current approaches have a substantial limitation: they do not take into account the temporal dimension. This is a strong limitation. For instance, the effects of two actions taken from different guidelines may potentially conflict, but practical conflicts happen only if effects of such actions overlaps in time. In this paper, we propose an approach to support the temporal detection of interactions. Artificial intelligence temporal reasoning techniques, based on temporal constraint propagation, are widely exploited to such a purpose.

Temporal reasoning techniques for the analysis of interactions in the treatment of comorbid patients

ANSELMA, LUCA;
2017

Abstract

Clinical practice guidelines are assuming a major role in the medical area, to provide physicians with evidence-based recommendations for the treatment of single pathologies. The treatment of comorbid patients (i.e., patients affected by multiple diseases) is one of the main challenges for the modern healthcare. It requires the development of new methodologies, supporting physicians in the treatment of interactions between guidelines. Several Artificial Intelligence approaches have started to face such a challenging problem. However, current approaches have a substantial limitation: they do not take into account the temporal dimension. This is a strong limitation. For instance, the effects of two actions taken from different guidelines may potentially conflict, but practical conflicts happen only if effects of such actions overlaps in time. In this paper, we propose an approach to support the temporal detection of interactions. Artificial intelligence temporal reasoning techniques, based on temporal constraint propagation, are widely exploited to such a purpose.
SAC '17: Proceedings of the Symposium on Applied Computing
Marrakech, Morocco
April, 2017
SAC '17: Proceedings of the Symposium on Applied Computing
ACM
971
976
9781450344869
http://dl.acm.org/citation.cfm?id=3019713&CFID=942455635&CFTOKEN=23389236
comorbidity treatment, computer-interpretable clinical guidelines, guideline interaction detection, medical knowledge representation, temporal reasoning
Anselma, Luca; Piovesan, Luca; Terenziani, Paolo
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1640616
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