Online assessment of content skill levels for medical texts

Authors:
Rey-Long Liu;Yun-Ling Lu
Affiliations:
Department of Medical Informatics, Tzu Chi University, Hualien, Taiwan, ROC;Computer Center, Chung Hua University, Hsinchu, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

Content skill levels of medical texts are essential for the comprehension (and hence utility) of medical information. A text that is too professional (i.e. high skill level) for a reader may be incomprehensible to the reader, and hence be of no value. Therefore, readers of different professional backgrounds require medical texts of different content skill levels. In this paper, we explore how content skill levels of medical texts may be assessed in an online manner without relying on any domain-dependent knowledge. We find that several assessment strategies have weaknesses, and propose an intelligent online assessment strategy OCSLA. Empirical evaluation on a medical text corpus from MedlinePlus shows that OCSLA may achieve both better and more fault-tolerant performance. The contributions are of practical significance to online medical text writing and recommendation, which are essential for heath education and promotion.