A Reuse-Based CBR System Evaluation in Critical Medical Scenarios

The early diagnosis and the correct therapy for generalized infections is an important factor for patient survival in intensive care burn units (ICBUs). Due to the number of pathologies involved, there is not a specific etiology and, therefore, it is difficult for physicians to quantify the patient severity to state the diagnosis. In this scenario, CBR finds problems to obtain a reliable solution when retrieved cases are highly similar. For example, in ICBU patients slight variations of monitored parameters have a deep impact on the patient's severity evaluation. Therefore, 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 on a retrospective analysis. However, these approaches do not seem to suffice when cases are very close. In this work, we propose and implement a CBR system to state the chance of a patient to survive. The system has been tested using a database of 89 patients from an ICBU, obtaining about 76% accuracy. Furthermore, in order to evaluate the behaviour of the CBR system in this kind of scenarios, we propose three techniques to obtain a reliance solution degree, one based on case retrieval and two based on case reuse.