title: Filtering for medical news items using a machine learning approach.
abstract: We address the problem of filtering medical news articles for targeted audiences. The approach is based on terms and one of the difficulties is extracting a feature set appropriate for the domain. This paper addresses the medical news-filtering problem using a machine learning approach. We describe the application of two supervised machine learning techniques, Decision Trees and Naïve Bayes, to automatically construct classifiers on the basis of a training set, in which news articles have been pre-classified by a medical expert and four other human readers. The goal is to classify the news articles into three groups: non-medical, medical intended for experts, and medical intended for other readers. While the general accuracy of the machine learning approach is around 78%, the accuracy of distinguishing non-medical articles from medical ones is shown to be 92%.
authors.affiliation: Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 1T8, Canada.
authors.forename: Wanhong
authors.initials: W
authors.lastname: Zheng
authors.forename: Evangelos
authors.initials: E
authors.lastname: Milios
authors.forename: Carolyn
authors.initials: C
authors.lastname: Watters
mesh_terms: D001185:Artificial Intelligence; D001499:Bayes Theorem; D003663:Decision Trees; D018156:Journalism, Medical; D016239:MEDLINE; D013358:Subject Headings; D018875:Vocabulary, Controlled