Abstract
The discovery of new and potentially meaningful relationships between concepts in the biomedical literature has attracted the attention of a lot of researchers in text mining. The main motivation is found in the increasing availability of the biomedical literature which makes it difficult for researchers in biomedicine to keep up with research progresses without the help of automatic knowledge discovery techniques. More than 14 million abstracts of this literature are contained in the Medline collection and are available online. In this paper we present the application of an association rule mining method to Medline abstracts in order to detect associations between concepts as indication of the existence of a biomedical relation among them. The discovery process fully exploits the MeSH (Medical Subject Headings) taxonomy, that is, a set of hierarchically related biomedical terms which permits to express associations at different levels of abstraction (generalized association rules). We report experimental results on a collection of abstracts obtained by querying Medline on a specific disease and we show the effectiveness of some filtering and browsing techniques designed to manage the huge amount of generalized associations that may be generated on real data.
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Berardi, M., Lapi, M., Leo, P., Loglisci, C. (2005). Mining Generalized Association Rules on Biomedical Literature. In: Ali, M., Esposito, F. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2005. Lecture Notes in Computer Science(), vol 3533. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11504894_68
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DOI: https://doi.org/10.1007/11504894_68
Publisher Name: Springer, Berlin, Heidelberg
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