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

Access to Document

10.1504/IJDMB.2009.026703

Cite this

@article{414cb7b09ef64378ae831957f0fb2dfc,

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",

year = "2009",

doi = "10.1504/IJDMB.2009.026703",

language = "English",

volume = "3",

pages = "299--313",

journal = "International Journal of Data Mining and Bioinformatics",

issn = "1748-5673",

publisher = "Inderscience Enterprises Ltd",

number = "3",

}

TY - JOUR

T1 - Clinical text classification under the open and closed topic assumptions

AU - Sasaki, Yutaka

AU - Rea, Brian

AU - Ananiadou, Sophia

PY - 2009

Y1 - 2009

N2 - 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.

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.

UR - https://www.scopus.com/pages/publications/68049116952

U2 - 10.1504/IJDMB.2009.026703

DO - 10.1504/IJDMB.2009.026703

M3 - Article

SN - 1748-5673

VL - 3

SP - 299

EP - 313

JO - International Journal of Data Mining and Bioinformatics

JF - International Journal of Data Mining and Bioinformatics

IS - 3

ER -

Clinical text classification under the open and closed topic assumptions

Abstract

Access to Document

Other files and links

Fingerprint

Cite this