Comparing Bayesian inference and case-based reasoning as support techniques in the diagnosis of Acute Bacterial Meningitis

The amount of information available for physicians has dramatically increased in the recent past. In contrast, the specialist’s ability to understand, synthesize and take into account such information is severely constrained by the short time available for the appointments. Therefore, systems reusing available knowledge and implementing reasoning processes become critical to support the tasks of the doctors. As a number of different techniques for building such systems are available, contrasting their effectiveness becomes a major concern. This is especially important in the case of infectious diseases that can be lethal within hours such as the Acute Bacterial Meningitis (ABM) for which implementing and contrasting different techniques allows for an increased reliability and speed in supporting the process of diagnosis. This work focuses on the construction of diagnosis support tools for ABM, reporting a comparative assessment of the quality of a Clinical Decision Support System (CDSS) resulting from the application of Case Based Reasoning (CBR), to that of an existing CDSS system developed using a Bayesian expert system. Although both approaches proved to be useful, the one based in CBR techniques show some interesting capabilities as higher precision, automatic learning or experience capturing, and also a better response to lack of input data. The three developed systems perform with high levels of accuracy– e.g. propose correct diagnostics based on a certain set of symptoms – but the one based on CBR present some additional capabilities that look very promising for implementing these kind of systems in a real world scenario.