Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
Exploiting Taxonomic and Causal Relations in Conversational Case Retrieval
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Refining Conversational Case Libraries
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Case-Based Approximate Reasoning (Theory and Decision Library B)
Case-Based Approximate Reasoning (Theory and Decision Library B)
Fuzzy theory approach for temporal model-based diagnosis: An application to medical domains
Artificial Intelligence in Medicine
Computing context-dependent temporal diagnosis in complex domains
Expert Systems with Applications: An International Journal
What evaluation criteria are right for CCBR? considering rank quality
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
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Case-based reasoning has demonstrated to be a suitable similarity-based approach to develop decision-support system in different domains. However, in certain scenarios CBR finds difficulties to obtain a reliable solution when retrieved cases are highly similar. For example, patients from an Intensive Care Unit are critical patients in which slight variations of monitored parameters have a deep impact on the patient severity evaluation. In this scenario, it seems necessary to extend the system outcome in order to indicate the reliance of the solution obtained. Main efforts in the literature for CBR evaluation focus on case retrieval (i.e. similarity) or a retrospective analysis. However, these approaches do not seem to suffice when cases are very close. To this end, we propose three techniques to obtain a reliance solution degree, one based on case retrieval and two based on case adaptation. We also show the capacities of this proposal in a medical problem.