Supporting discovery in medicine by association rule mining in Medline and UMLS

The paper presents an interactive discovery support system for the field of medicine. The intended users of the system are medical researchers. The goal of the system is: for a given starting concept of interest, discover new, potentially meaningful relations with other concepts that have not been published in the medical literature before. The known relations between the medical concepts come from the Medline bibliographic database and the UMLS. We use association rules for discovering the relationship between medical concepts. We evaluated the system by testing how successfully it predicted future discoveries (new relations between concepts). We first divided the Medline database into two segments (older and newer) using the publication date. Then we calculated how many of the new relations found by the system in the older segment become known relations in the newer segment. We found out with statistical significance that the system predicts new relations better then someone predicting randomly. The evaluation showed that our approach for supporting discovery in medicine is successful, but also that some improvements are needed, especially on limiting the number of potential discoveries the system generates.