Information Retrieval in Medicine: An Extensive Experimental Study

Roberto Gatta, Mauro Vallati, Berardino De Bari, Nadia Pasinetti, Carlo Cappelli, Ilenia Pirola, Massimo Salvetti, Michela Buglione, Maria Lorenza Muiesan, Stefano Magrini, Maurizio Castellano

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)


The clinical documents stored in a textual and unstructured manner represent a precious source of information that can be gathered by exploiting Information Retrieval techniques. Classification algorithms, and their composition through Ensemble Methods, can be used for organizing this huge amount of data, but are usually tested on standardized corpora, which significantly differ from actual clinical documents that can be found in a modern hospital. In this paper we present the results of a large experimental analysis conducted on 36,000 clinical documents, generated by three different medical Departments. For the sake of this investigation we propose a new classifier, based on the entropy idea, and test four single algorithms and four ensemble methods. The experimental results show the performance of selected approaches in a real-world environment, and highlights the impact of obsolescence on classification.
Original languageEnglish
Title of host publicationProceedings of the International Conference on Health Informatics
Place of PublicationAngers, France
Number of pages6
Publication statusPublished - 2014
Event7th International Conference on Health Informatics - ESEO, Angers, France
Duration: 3 Mar 20146 Mar 2014 (Link to Conference Website)


Conference7th International Conference on Health Informatics
Abbreviated titleHEALTHINF 2014
Internet address


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