Improving Perceived and Actual Text Difficulty for Health Information Consumers using Semi-Automated Methods
Information Systems and Technology (CGU)
Databases and Information Systems | Health Information Technology
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
© 2012 American Medical Informatics Association
G. Leroy, J.E. Endicott, O. Mouradi, D. Kauchak, and M. Just, "Improving Perceived and Actual Text Difficulty for Health Information Consumers using Semi-Automated Methods", American Medical Informatics Association (AMIA) Fall Symposium, Chicago, IL, November 3-7, 2012.