Extracting epidemiologic exposure and outcome terms from literature using machine learning approaches
by Yanxin Lu; Hua Xu; Neeraja B. Peterson; Qi Dai; Min Jiang; Joshua C. Denny; Mei Liu
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 6, No. 4, 2012

Abstract: Much epidemiologic information resides in literature, which is not in a computable format. To extract information and build knowledge bases of epidemiologic studies, we developed a system to extract noun phrases about epidemiologic exposures and outcomes. The system consists of two components: a natural language processing (NLP) engine a machine learning (ML) based classifier. Four ML algorithms were applied and compared over different feature sets. To evaluate the performance of the system, we manually constructed an annotated dataset. The system achieved the highest F-measure of 82.0% for extracting exposure terms, and 70% for extracting outcome terms.

Online publication date: Wed, 17-Dec-2014

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Mining and Bioinformatics (IJDMB):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com