Extracting topical phrases from clinical documents

He, Yulan (2016). Extracting topical phrases from clinical documents. IN: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16). AAAI.

Abstract

In clinical documents, medical terms are often expressed in multi-word phrases. Traditional topic modelling approaches relying on the “bag-of-words” assumption are not effective in extracting topic themes from clinical documents. This paper proposes to first extract medical phrases using an off-the-shelf tool for medical concept mention extraction, and then train a topic model which takes a hierarchy of Pitman-Yor processes as prior for modelling the generation of phrases of arbitrary length. Experimental results on patients’ discharge summaries show that the proposed approach outperforms the state-of-the-art topical phrase extraction model on both perplexity and topic coherence measure and finds more interpretable topics.

Divisions: Engineering & Applied Sciences > Computer science
Engineering & Applied Sciences > Systems analytics research institute (SARI)
Engineering & Applied Sciences > Computer science research group
Additional Information: -
Event Title: 30th AAAI Conference on Artificial Intelligence, AAAI 2016
Event Type: Other
Event Dates: 2016-02-12 - 2016-02-17
Uncontrolled Keywords: Artificial Intelligence
Full Text Link: http://www.aaai ... s/16He11771.pdf
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
Published Date: 2016-02-12
Authors: He, Yulan ( 0000-0003-3948-5845)

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