Improving perceived and actual text difficulty for health information consumers using semi-automated methods.

Gondy Augusta Leroy, James E. Endicott, Obay Mouradi, David Kauchak, Melissa L. Just

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

We are developing algorithms for semi-automated simplification of medical text. Based on lexical and grammatical corpus analysis, we identified a new metric, term familiarity, to help estimate text difficulty. We developed an algorithm that uses term familiarity to identify difficult text and select easier alternatives from lexical resources such as WordNet, UMLS and Wiktionary. Twelve sentences were simplified to measure perceived difficulty using a 5-point Likert scale. Two documents were simplified to measure actual difficulty by posing questions with and without the text present (information understanding and retention). We conducted a user study by inviting participants (N=84) via Amazon Mechanical Turk. There was a significant effect of simplification on perceived difficulty (p<.001). We also saw slightly improved understanding with better question-answering for simplified documents but the effect was not significant (p=.097). Our results show how term familiarity is a valuable component in simplifying text in an efficient and scalable manner.

Original languageEnglish (US)
Pages (from-to)522-531
Number of pages10
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
Volume2012
StatePublished - 2012
Externally publishedYes

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Consumer Health Information
Unified Medical Language System
Recognition (Psychology)

ASJC Scopus subject areas

  • Medicine(all)

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Improving perceived and actual text difficulty for health information consumers using semi-automated methods. / Leroy, Gondy Augusta; Endicott, James E.; Mouradi, Obay; Kauchak, David; Just, Melissa L.

In: AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium, Vol. 2012, 2012, p. 522-531.

Research output: Contribution to journalArticle

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