Online assessment of content skill levels for medical texts

被引:1
|
作者
Liu, Rey-Long [1 ]
Lu, Yun-Ling [2 ]
机构
[1] Tzu Chi Univ, Dept Med Informat, Hualien, Taiwan
[2] Chung Hua Univ, Ctr Comp, Hsinchu, Taiwan
关键词
Medical texts; Content skill level assessment; Medical text recommendation; Medical text writing; HEALTH INFORMATION; LITERACY; WEB;
D O I
10.1016/j.eswa.2009.04.060
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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. (C) 2009 Elsevier Ltd. All rights reserved.
引用
收藏
页码:12272 / 12280
页数:9
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