title: Automatically classifying question types for consumer health questions.
abstract: We present a method for automatically classifying consumer health questions. Our thirteen question types are designed to aid in the automatic retrieval of medical answers from consumer health resources. To our knowledge, this is the first machine learning-based method specifically for classifying consumer health questions. We demonstrate how previous approaches to medical question classification are insufficient to achieve high accuracy on this task. Additionally, we describe, manually annotate, and automatically classify three important question elements that improve question classification over previous techniques. Our results and analysis illustrate the difficulty of the task and the future directions that are necessary to achieve high-performing consumer health question classification.
authors.affiliation: U.S. National Library of Medicine, Bethesda, MD.
authors.forename: Kirk
authors.initials: K
authors.lastname: Roberts
authors.affiliation: U.S. National Library of Medicine, Bethesda, MD.
authors.forename: Halil
authors.initials: H
authors.lastname: Kilicoglu
authors.affiliation: U.S. National Library of Medicine, Bethesda, MD.
authors.forename: Marcelo
authors.initials: M
authors.lastname: Fiszman
authors.affiliation: U.S. National Library of Medicine, Bethesda, MD.
authors.forename: Dina
authors.initials: D
authors.lastname: Demner-Fushman
mesh_terms: D054626:Consumer Health Information; D006801:Humans; D057226:Information Seeking Behavior / Q000145:classification; D016247:Information Storage and Retrieval / Q000145:classification*; D009323:Natural Language Processing