Case-based reasoning for antibiotics therapy advice: an investigation of retrieval algorithms and prototypes

We have developed an antibiotics therapy advice system called ICONS for patients in an intensive care unit (ICU) who have caught an infection as additional complication. Since advice for such critically ill patients is needed very quickly and as the actual pathogen still has to be identified by the laboratory, we use an expected pathogen spectrum based on medical background knowledge and known resistances. The expected pathogen spectra and the resistance information are periodically updated from laboratory results. To speed up the process of finding suitable therapy recommendations, we have applied case-based reasoning (CBR) techniques. As all required information should always be up to date in medical expert systems, new cases should be incrementally incorporated into the case base and outdated ones should be updated or erased. For reasons of space limitations and of retrieval time an indefinite growth of the case base should be avoided. To fulfill these requirements we propose that specific single cases should be generalised to more general prototypical ones and that subsequent redundant cases should be erased. In this paper, we present evaluation results of different generation strategies for generalised cases (prototypes). Additionally, we compare measured retrieval times for two indexing retrieval algorithms: simple indexing, which is appropriate for small and medium case bases, and tree-hash retrieval, which is advantageous for large case bases.