Clinical text classification under the open and closed topic assumptions

Yutaka Sasaki; Brian Rea; Sophia Ananiadou

doi:10.1504/IJDMB.2009.026703

Clinical text classification under the open and closed topic assumptions

Yutaka Sasaki, Brian Rea, Sophia Ananiadou

Research output: Contribution to journal › Article › peer-review

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. Copyright © 2009 Inderscience Enterprises Ltd.

Original language	English
Pages (from-to)	299-313
Number of pages	14
Journal	International Journal of Data Mining and Bioinformatics
Volume	3
Issue number	3
DOIs	https://doi.org/10.1504/IJDMB.2009.026703
Publication status	Published - 2009

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title = "Clinical text classification under the open and closed topic assumptions",

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. Copyright {\textcopyright} 2009 Inderscience Enterprises Ltd.",

author = "Yutaka Sasaki and Brian Rea and Sophia Ananiadou",

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AB - 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. Copyright © 2009 Inderscience Enterprises Ltd.

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DO - 10.1504/IJDMB.2009.026703

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EP - 313

JO - International Journal of Data Mining and Bioinformatics

JF - International Journal of Data Mining and Bioinformatics

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ER -

Clinical text classification under the open and closed topic assumptions

Abstract

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