Improving Text Retrieval in Medical Collections Through Automatic Categorization

@incollection{citeulike:2910557, abstract = {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 difficulty 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 networks, that allows combining information derived from the text of the medical documents with information on the diseases related to these documents (obtained from an automatic categorization method). This leads to a new ranking formula which we evaluate using a medical reference collection, the OHSUMED collection. Our results indicate that this combination of evidences might yield considerable gains in retrieval performance. When the queries are strongly related to diseases, these gains might be as high as 84\%. This 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.}, author = {Vale, Rodrigo F. and Ribeiro-Neto, Berthier A. and de Lima, Luciano R. and Laender, Alberto H. and Junior, Hermes R. }, citeulike-article-id = {2910557}, journal = {String Processing and Information Retrieval}, keywords = {health, txtmining}, pages = {197--210}, posted-at = {2008-06-20 14:57:20}, priority = {5}, title = {Improving Text Retrieval in Medical Collections Through Automatic Categorization}, url = {http://www.springerlink.com/content/2dmxmwtv1d3qvjbk }, year = {2003} }

TY - CHAP ID - citeulike:2910557 L3 - citeulike-article-id:2910557 TI - Improving Text Retrieval in Medical Collections Through Automatic Categorization JF - String Processing and Information Retrieval SP - 197 EP - 210 N2 - 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 difficulty 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 networks, that allows combining information derived from the text of the medical documents with information on the diseases related to these documents (obtained from an automatic categorization method). This leads to a new ranking formula which we evaluate using a medical reference collection, the OHSUMED collection. Our results indicate that this combination of evidences might yield considerable gains in retrieval performance. When the queries are strongly related to diseases, these gains might be as high as 84%. This 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. KW - health KW - txtmining AU - Vale, Rodrigo AU - Ribeiro-Neto, Berthier AU - de Lima, Luciano AU - Laender, Alberto AU - Junior, Hermes PY - 2003/// UR - http://www.springerlink.com/content/2dmxmwtv1d3qvjbk ER -