title: Extracting epidemiologic exposure and outcome terms from literature using machine learning approaches.
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.
authors.affiliation: Department of Human Anatomy, Histology and Embryology, Fudan University, 138 Yi Xue Yuan Road, Shanghai 200032, China. yanxin.m.lu@gmail.com
authors.forename: Yanxin
authors.initials: Y
authors.lastname: Lu
authors.forename: Hua
authors.initials: H
authors.lastname: Xu
authors.forename: Neeraja B
authors.initials: NB
authors.lastname: Peterson
authors.forename: Qi
authors.initials: Q
authors.lastname: Dai
authors.forename: Min
authors.initials: M
authors.lastname: Jiang
authors.forename: Joshua C
authors.initials: JC
authors.lastname: Denny
authors.forename: Mei
authors.initials: M
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D015981:Epidemiologic Factors; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D051188:Knowledge Bases; D009323:Natural Language Processing