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Journal of Healthcare Engineering
Volume 2017 (2017), Article ID 4307508, 14 pages
https://doi.org/10.1155/2017/4307508
Research Article

Clinical Decision Support System for Diabetes Based on Ontology Reasoning and TOPSIS Analysis

1Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan
2College of Computer and Information Engineering, Xiamen University of Technology, No. 600, Ligong Rd., Jimei District, Xiamen, Fujian, China
3Library, Chienkuo Technology University, Changhua, Taiwan
4Taichung Hospital, Ministry of Health and Welfare, Executive Yuan, Taichung, Taiwan

Correspondence should be addressed to Rung-Ching Chen; wt.ude.tuyc@gnihcrc

Received 2 July 2017; Accepted 10 September 2017; Published 26 October 2017

Academic Editor: Weide Chang

Copyright © 2017 Rung-Ching Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Introduction. Although a number of researchers have considered the positive potential of Clinical Decision Support System (CDSS), they did not consider that patients’ attitude which leads to active treatment strategies or HbA1c targets. Materials and Methods. We adopted the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) published to propose an HbA1c target and antidiabetic medication recommendation system for patients. Based on the antidiabetic medication profiles, which were presented by the American Association of Clinical Endocrinologists (AACE) and American College of Endocrinology (ACE), we use TOPSIS to calculate the ranking of antidiabetic medications. Results. The endocrinologist set up ten virtual patients’ medical data to evaluate a decision support system. The system indicates that the CDSS performs well and is useful to 87%, and the recommendation system is suitable for outpatients. The evaluation results of the antidiabetic medications show that the system has 85% satisfaction degree which can assist clinicians to manage T2DM while selecting antidiabetic medications. Conclusions. In addition to aiding doctors’ clinical diagnosis, the system not only can serve as a guide for specialty physicians but also can help nonspecialty doctors and young doctors with their drug prescriptions.