ArticleJournal of healthcare engineering2017
Clinical Decision Support System for Diabetes Based on Ontology Reasoning and TOPSIS Analysis.
Article in Journal of healthcare engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Machine learning driven diabetes care using predictive-prescriptive analytics for personalized medication prescription.Scientific reports · 2025Article
- A drug prescription recommendation system based on novel DIAKID ontology and extensive semantic rules.Health information science and systems · 2024Article
- Assessing the Utility, Impact, and Adoption Challenges of an Artificial Intelligence-Enabled Prescription Advisory Tool for Type 2 Diabetes Management: Qualitative Study.JMIR human factors · 2024Article
- Ontologies Applied in Clinical Decision Support System Rules: Systematic Review.JMIR medical informatics · 2023Review
- [Ontologies Applied in Clinical Decision Support Systems for Diabetes].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2023Review
- Professional medical education approaches: mobilizing evidence for clinicians.Frontiers in medicine · 2023Review
- A Personalized Ontology-Based Decision Support System for Complex Chronic Patients: Retrospective Observational Study.JMIR formative research · 2022Article
- Design of Intelligent Nursing Decision Support System Based on Multiattribute Decision Model.Journal of healthcare engineering · 2022Article
- Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges.Diagnostics (Basel, Switzerland) · 2021Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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 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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Registered trials
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