Clinical text classification under the Open and Closed Topic Assumptions

Authors:
Yutaka Sasaki;Brian Rea;Sophia Ananiadou
Affiliations:
School of Computer Science, University of Manchester MIB, 131 Princess Street, Manchester, M1 7DN, UK.;National Centre for Text Mining, School of Computer Science, University of Manchester MIB, 131 Princess Street, Manchester, M1 7DN, UK.;National Centre for Text Mining, School of Computer Science, University of Manchester MIB, 131 Princess Street, Manchester, M1 7DN, UK
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2009

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Abstract

This paper investigates multi-topic aspects in automatic classification of clinical free text in comparison with general text. In this paper, we facilitate two different views on multi-topics: the Closed Topic Assumption (CTA) and the Open Topic Assumption (OTA). Experimental results show that the characteristics of multi-topic assignments in the Computational Medicine Centre (CMC) Medical NLP Challenge Data is strongly OTA-oriented but general text Reuters-21578 is characterised in the middle of the OTA and CTA spectrum.