In this work, we propose a method for the automated expert system creation. The method is based on the integration of global knowledge (i.e. knowledge from the field experts) and local knowledge (i.e. knowledge derived from the available data) in a single inference engine. Starting from an initial set of rules (expert's knowledge) and an annotated dataset, data mining is performed to the dataset and a second set of rules is acquired. Both of them are integrated into a single set of rules. Fuzzy modeling is then applied to the rules, transforming them into a fuzzy model, and finally, an optimization technique is used to tune the fuzzy model's parameters. The method is applied to a medical domain problem, the cardiac arrhythmic beat classification and satisfactory results have been obtained. The method experiences several advantages compared to approaches based solely on expert's knowledge or mined knowledge while the ability to interpret the decisions made from the created fuzzy expert system is a major advantage compared to "black box" approaches.