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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2017
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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. |
format | Online Article Text |
id | pubmed-5682097 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
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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