Recuperação de informação em coleções médicas utilizando categorização automática de documentos

Rodrigo ValeLuciano LimaBerthier R. NetoAlberto LaenderHermes Junior

A current and important research issue is the retrieval of relevant medical information. In fact, while the medical knowledge expands at a rate never observed before, its diffusion is slow. One of the main reasons is the dif?culty in locating the relevant information in the modern and large medical text collections of today. In this work, we introduce a framework, based on Bayesian belief networks, that allows combining information derived from the text of the documents with information on the diseases related to the medical documents (obtained from an automatic categorization process). This leads to a new ranking method which we evaluate using a medical reference collection (the Oshumed collection). Our results indicate that this combination of evidences (document text and diseases related to the document) might yield considerable gains in retrieval performance. When the queries are strongly related to diseases, these gains might be as high as 84%. Our approach is quite distinct from previous ones and shows that information generated by an automatic categorization procedure can be used effectively to improve the quality of the answers provided by an information retrieval (IR) system specialized in the medical domain.

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