1. Readability of Arabic Medicine Information Leaflets: A Machine Learning Approach
- Author
-
Nora Abanmy, Sinaa Alageel, Hend S. Al-Khalifa, Maha Al-Yahya, and Sihaam Alotaibi
- Subjects
Computer science ,Arabic ,media_common.quotation_subject ,02 engineering and technology ,computer.software_genre ,Machine learning ,Machine Learning ,03 medical and health sciences ,0302 clinical medicine ,Reading (process) ,0202 electrical engineering, electronic engineering, information engineering ,030212 general & internal medicine ,cardiovascular diseases ,Arabic Language ,General Environmental Science ,media_common ,Arabic Natural Language Processing ,Multimedia ,business.industry ,technology, industry, and agriculture ,Readability ,language.human_language ,Medicine Information Leaflets ,Text Readability ,language ,cardiovascular system ,General Earth and Planetary Sciences ,020201 artificial intelligence & image processing ,lipids (amino acids, peptides, and proteins) ,Artificial intelligence ,business ,computer ,Natural language processing - Abstract
This paper presents a project that explores the possibility of assessing the readability level of Arabic medicine information leaflets using machine learning techniques. There are a number of popular readability formulas and tools that have been successfully used to assess the readability of health-related information in several languages. However, there is limited work on the readability assessment of health-related information, specifically medicine information leaflets in Arabic. We describe the design of a tool that uses machine learning to assess the readability of medicine information leaflets. We utilize a corpus comprising 1112 medicine information leaflets annotated with three difficulty levels. Based on a study of existing literature, we selected a number of features influencing text difficulty. The tool will help specialized organizations in medicine information leaflets production to produce the leaflets at appropriate level of reading for the majority of leaflets consumers.
- Published
- 2016
- Full Text
- View/download PDF