How to use contextual knowledge in medical case-based reasoning systems: a survey on very recent trends

Objectives: This paper aims at systematizing the ways in which the contextual knowledge embedded in the case library can support decision making, within case-based reasoning (CBR) systems. In particular, CBR applications to the medical domain are considered. Methods and material: After a quick survey on the definition and on the role of context in artificial intelligence research, we have focused on CBR, with a particular emphasis on medical applications. In this field, we have identified a number of very recent contributions, which strongly recognize context per se as a major knowledge source. These contributions propose to maintain and to rely on contextual information, in order to support human reasoning in different fashions. Results: We have distinguished three main directions in which contextual knowledge can be resorted to, in order to optimize physicians' decision making. Such directions can be summarized as follows: (1) to reduce the search space in the case retrieval step; (2) to maintain the overall knowledge content always valid and up to date, and (3) to adapt knowledge application and reasoning to local/personal constraints. We have also properly categorized the surveyed works within these three clusters, and identified the most significant ones, able to exploit contextual knowledge along more than one direction. Conclusions: Innovative applications of the contextual knowledge recorded in the case library, described and systematized in this paper, can trace promising research directions for the future.