Automatically classifying question types for consumer health questions

Kirk Roberts, Halil Kilicoglu, Marcelo Fiszman, Dina Demner-Fushman

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)1018-1027
Number of pages10
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
Volume2014
StatePublished - Jan 1 2014
Externally publishedYes

ASJC Scopus subject areas

  • Medicine(all)

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