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

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.