Clinical text classification under the open and closed topic assumptions

Yutaka Sasaki, Brian Rea, Sophia Ananiadou

    Research output: Contribution to journalArticlepeer-review


    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 languageEnglish
    Pages (from-to)299-313
    Number of pages14
    JournalInternational Journal of Data Mining and Bioinformatics
    Issue number3
    Publication statusPublished - 2009


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