title: Automatically Assessing the Expert Degree of Online Health Content using SVMs.
abstract: More and more people search for health information regarding diseases, diagnoses and treatments over the Web. However, lay people often have difficulties in assessing the understandability of related articles. Therefore, they could benefit from a system, which computes the medical expert degree of a corresponding piece of text in advance. In this paper we present an approach to automatically compute this expert degree using a machine learning approach. For evaluation purposes we constructed a large text corpus and tested our trained text classifier, which is based on Support Vector Machines.
authors.affiliation: Dept. of Medical Informatics, Heilbronn University, Heilbronn, Germany.
authors.forename: Richard
authors.initials: R
authors.lastname: Zowalla
authors.affiliation: Dept. of Medical Informatics, Heilbronn University, Heilbronn, Germany.
authors.forename: Martin
authors.initials: M
authors.lastname: Wiesner
authors.affiliation: Dept. of Medical Informatics, Heilbronn University, Heilbronn, Germany.
authors.forename: Daniel
authors.initials: D
authors.lastname: Pfeifer
mesh_terms: D054626:Consumer Health Information / Q000145:classification*; D005104:Expert Testimony; D063005:Health Information Systems / Q000145:classification; D020407:Internet / Q000145:classification*; D009323:Natural Language Processing; D009862:Online Systems / Q000145:classification*; D010363:Pattern Recognition, Automated / Q000379:methods; D060388:Support Vector Machine