Diabetic patients management exploiting case-based reasoning techniques

In this paper we propose a case-based decision support tool, designed to help physicians in 1st type diabetes therapy revision through the intelligent retrieval of data related to past situations (or 'cases') similar to the current one. A case is defined as a set of variable values (or features) collected during a visit. We defined taxonomy of prototypical patients' conditions, or classes, to which each case should belong. For each input case, the system allows the physician to find similar past cases, both from the same patient and from different ones. We have implemented a two-steps procedure; (1) it finds the classes to which the input case could belong; (2) it lists the most similar cases from these classes, through a nearest neighbor technique, and provides some statistics useful for decision taking. The performance of the system has been tested on a data-base of 147 real cases, collected at the Policlinico S. Matteo Hospital of Pavia. The tool is fully integrated in the web-based architecture of the EU funded Telematic management of Insulin Dependent Diabetes Mellitus (T-IDDM) project.