A recommendation system based on domain ontology and SWRL for anti-diabetic drugs selection

  • Authors:

  • Rung-Ching Chen;Yun-Hou Huang;Cho-Tsan Bau;Shyi-Ming Chen

  • Affiliations:

  • Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan;Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan;Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan and Division of Endocrinology and Metabolism, Department of Medicine, Taichung Hospital, Department of Hea ...;Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan and Graduate Institute of Educational Measurement and Statistics, N ...

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2012

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Abstract

Diabetes mellitus is one of the most common chronic diseases in recent years. According to the World Health Organization, estimated diabetic patient numbers will increase by 56 percent in Asia from the year 2010 to 2025. Mean while, the number of anti-diabetic drugs that doctors are able to utilize also increase as the development of pharmaceutical drugs. In this paper, we present a Diabetes Medication Recommendation system, based on domain ontology, that employ the knowledge base provided by a hospital specialist in Taichung's Department of Health and the database of the American Association of Clinical Endocrinologists Medical Guidelines for Clinical Practice for the Management of Diabetes Mellitus (AACEMG). By thorough analysis, the system first builds ontology knowledge about the drugs' nature attributes, type of dispensing and side effects, and ontology knowledge about patients' symptoms. It then utilizes Semantic Web Rule Language (SWRL) and Java Expert System Shell (JESS) to induce potential prescriptions for the patients. This system is able to analyze the symptoms of diabetes as well as to select the most appropriate drug from related drugs.