A classifier to evaluate language specificity of medical documents

Trudi Miller, Gondy Leroy, Samir Chatterjee, Fan Jie, Brian Thoms

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Scopus citations

Abstract

Consumer health information written by health care professionals is often inaccessible to the consumers it is written for. Traditional readability formulas examine syntactic features like sentence length and number of syllables, ignoring the target audience's grasp of the words themselves. The use of specialized vocabulary disrupts the understanding of patients with low reading skills, causing a decrease in comprehension. A naïve Bayes classifier for three levels of increasing medical terminology specificity (consumer/patient, novice health learner, medical professional) was created with a lexicon generated from a representative medical corpus. Ninety-six percent accuracy in classification was attained. The classifier was then applied to existing consumer health web pages. We found that only 4% of pages were classified at a layperson level, regardless of the Flesch reading ease scores, while the remaining pages were at the level of medical professionals. This indicates that consumer health web pages are not using appropriate language for their target audience.

Original languageEnglish (US)
Title of host publicationProceedings of the 40th Annual Hawaii International Conference on System Sciences 2007, HICSS'07
DOIs
StatePublished - 2007
Externally publishedYes
Event40th Annual Hawaii International Conference on System Sciences 2007, HICSS'07 - Big Island, HI, United States
Duration: Jan 3 2007Jan 6 2007

Publication series

NameProceedings of the Annual Hawaii International Conference on System Sciences
ISSN (Print)1530-1605

Other

Other40th Annual Hawaii International Conference on System Sciences 2007, HICSS'07
Country/TerritoryUnited States
CityBig Island, HI
Period1/3/071/6/07

ASJC Scopus subject areas

  • Engineering(all)

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