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

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 As...

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Autores principales: Chen, Rung-Ching, Jiang, Hui Qin, Huang, Chung-Yi, Bau, Cho-Tsan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5682097/
https://www.ncbi.nlm.nih.gov/pubmed/29312655
http://dx.doi.org/10.1155/2017/4307508
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author Chen, Rung-Ching
Jiang, Hui Qin
Huang, Chung-Yi
Bau, Cho-Tsan
author_facet Chen, Rung-Ching
Jiang, Hui Qin
Huang, Chung-Yi
Bau, Cho-Tsan
author_sort Chen, Rung-Ching
collection PubMed
description 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.
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spelling pubmed-56820972018-01-08 Clinical Decision Support System for Diabetes Based on Ontology Reasoning and TOPSIS Analysis Chen, Rung-Ching Jiang, Hui Qin Huang, Chung-Yi Bau, Cho-Tsan J Healthc Eng Research Article 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. Hindawi 2017 2017-10-26 /pmc/articles/PMC5682097/ /pubmed/29312655 http://dx.doi.org/10.1155/2017/4307508 Text en Copyright © 2017 Rung-Ching Chen et al. http://creativecommons.org/licenses/by/4.0/ 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.
spellingShingle Research Article
Chen, Rung-Ching
Jiang, Hui Qin
Huang, Chung-Yi
Bau, Cho-Tsan
Clinical Decision Support System for Diabetes Based on Ontology Reasoning and TOPSIS Analysis
title Clinical Decision Support System for Diabetes Based on Ontology Reasoning and TOPSIS Analysis
title_full Clinical Decision Support System for Diabetes Based on Ontology Reasoning and TOPSIS Analysis
title_fullStr Clinical Decision Support System for Diabetes Based on Ontology Reasoning and TOPSIS Analysis
title_full_unstemmed Clinical Decision Support System for Diabetes Based on Ontology Reasoning and TOPSIS Analysis
title_short Clinical Decision Support System for Diabetes Based on Ontology Reasoning and TOPSIS Analysis
title_sort clinical decision support system for diabetes based on ontology reasoning and topsis analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5682097/
https://www.ncbi.nlm.nih.gov/pubmed/29312655
http://dx.doi.org/10.1155/2017/4307508
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