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Authors: Roberto Gatta 1 ; Mauro Vallati 2 ; Berardino De Bari 1 ; Nadia Pasinetti 1 ; Carlo Cappelli 1 ; Ilenia Pirola 1 ; Massimo Salvetti 1 ; Michela Buglione 1 ; Maria L. Muiesan 1 ; Stefano M. Magrini 1 and Maurizio Castellano 1

Affiliations: 1 University of Brescia, Italy ; 2 University of Huddersfield, United Kingdom

Keyword(s): Information Retrieval, Text Categorization, Document Classification.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Business Analytics ; Data Engineering ; Data Mining ; Databases and Datawarehousing ; Databases and Information Systems Integration ; Datamining ; Enterprise Information Systems ; Health Information Systems ; Pattern Recognition and Machine Learning ; Sensor Networks ; Signal Processing ; Soft Computing

Abstract: 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.

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Paper citation in several formats:
Gatta, R.; Vallati, M.; De Bari, B.; Pasinetti, N.; Cappelli, C.; Pirola, I.; Salvetti, M.; Buglione, M.; Muiesan, M.; Magrini, S. and Castellano, M. (2014). Information Retrieval in Medicine - An Extensive Experimental Study. In Proceedings of the International Conference on Health Informatics (BIOSTEC 2014) - HEALTHINF; ISBN 978-989-758-010-9; ISSN 2184-4305, SciTePress, pages 447-452. DOI: 10.5220/0004909904470452

@conference{healthinf14,
author={Roberto Gatta. and Mauro Vallati. and Berardino {De Bari}. and Nadia Pasinetti. and Carlo Cappelli. and Ilenia Pirola. and Massimo Salvetti. and Michela Buglione. and Maria L. Muiesan. and Stefano M. Magrini. and Maurizio Castellano.},
title={Information Retrieval in Medicine - An Extensive Experimental Study},
booktitle={Proceedings of the International Conference on Health Informatics (BIOSTEC 2014) - HEALTHINF},
year={2014},
pages={447-452},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004909904470452},
isbn={978-989-758-010-9},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the International Conference on Health Informatics (BIOSTEC 2014) - HEALTHINF
TI - Information Retrieval in Medicine - An Extensive Experimental Study
SN - 978-989-758-010-9
IS - 2184-4305
AU - Gatta, R.
AU - Vallati, M.
AU - De Bari, B.
AU - Pasinetti, N.
AU - Cappelli, C.
AU - Pirola, I.
AU - Salvetti, M.
AU - Buglione, M.
AU - Muiesan, M.
AU - Magrini, S.
AU - Castellano, M.
PY - 2014
SP - 447
EP - 452
DO - 10.5220/0004909904470452
PB - SciTePress