Exploring new roles for case-based reasoning in heterogeneous AI systems for medical decision support
作者:Stefania Montani
摘要
Background Supporting medical decision making is a complex task, that offers challenging research issues to Artificial Intelligence (AI) scientists. The Case-based Reasoning (CBR) methodology has been proposed as a possible means for supporting decision making in this domain since the 1980s. Nevertheless, despite the variety of efforts produced by the CBR research community, and the number of issues properly handled by means of this methodology, the success of CBR systems in medicine is somehow limited, and almost no research product has been fully tested and commercialized; one of the main reasons for this may be found in the nature of the problem domain, which is extremely complex and multi-faceted.
论文关键词:Case Library, Temporal Abstraction, State Abstraction, Medical Decision Support, Knowledge Acquisition Bottleneck
论文评审过程:
论文官网地址:https://doi.org/10.1007/s10489-007-0046-2