Abstract
Clinical decision support systems (CDSS) assist medical practitioners in their daily work, thereby enhancing the quality of care given to a patient. It supports them in the decision-making process and suggests appropriate treatments. The use of the ontology to build knowledge-driven decision support systems is widely adopted. Ontology is best suited to encapsulate the concepts and relationships of terms associated with the medical domain. It is suitable for capturing medical knowledge in a formal way, allowing sharing and reusing it whenever necessary. All concepts and relationships detailed in clinical guidelines can be implemented using Web Ontology Language (OWL). The reasoning mechanism is vital in any knowledge-based system. Ontology can be reasoned to recommend the suitable treatment for a patient by considering the current medical status of the patient. OntoDiabetic, an ontology-based decision support system is developed to assess the risk factors and provide appropriate treatment suggestions for diabetic patients. This paper focuses on the modeling and implementation of clinical guidelines using OWL2 rules and the reasoning process of the OntoDiabetic system. The case study is conducted for patients having the risk of overt cardiovascular disease, diabetic nephropathy and hypertension in primary health centers of Oman.
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Reddy, K.: Developing Reliable Clinical Diagnosis Support System. Retrieved from http://www.kiranreddys.com/articles/clinicaldiagnosissupportsystems.pdf . Accessed 4 April (2014)
Abbasi, M.M.; Kashiyarndi, S.: Clinical Decision Support Systems: A Discussion on Different Methodologies Used in Health Care. Marlaedalen University Sweden. Available at: http://www.idt.mdh.se/kurser/ct3340/ht10/FinalPapers/15-AbbasiKashiyarndi.Pdf (2006)
Berner, E.S. (eds): Clinical Decision Support Systems: Theory and Practice. Springer Science & Business Media, NewYork, NY, USA (2007)
Doulaverakis C. et al.: IVUS image processing and semantic analysis for Cardiovascular Diseases risk prediction. Int. J. Biomed. Eng. Technol. 3(3–4), 349–374 (2010)
Zhang X. et al.: Ontology-based context modeling for emotion recognition in an intelligent web. World Wide Web 16(4), 497–513 (2013)
Isern D., Sánchez D., Moreno A.: Ontology-driven execution of clinical guidelines. Comput. Methods Progr. Biomed. 107(2), 122–139 (2012)
Chang Y.-J. et al.: Cross-domain probabilistic inference in a clinical decision support system: examples for dermatology and rheumatology. Comput. Methods Progr. Biomed. 104(2), 286–291 (2011)
Wang, X.H. et al.: Ontology based context modeling and reasoning using OWL. Pervasive computing and communications workshops, 2004. In: Proceedings of the second IEEE annual conference on IEEE (2004)
O’Connor, M. et al.: Using semantic web technologies for knowledge-driven querying of biomedical data. In: Bellazi, R., Abu-Hanna, A., Hunter, J. (eds.) Artificial Intelligence in Medicine, pp. 267–276. Springer, Berlin, Heidelberg (2007)
Kuo K.-L., Fuh C.-S.: A rule-based clinical decision model to support interpretation of multiple data in health examinations. J. Med. Syst. 35(6), 1359–1373 (2011)
Mash, B.; De Vries, E.; Abdul, I.: Diabetes in Africa: the new pandemic. Report on the 19th World Diabetes Congress, Cape Town, December 2006. South African Family Practice 49(6), 44–50 (2007)
Iran: Homosexuality Linked To Diabetes, World News Daily Report, Retrieved from http://worldnewsdailyreport.com/iran-homosexuality-linked-to-diabetes/ Accessed 10 Oct (2014)
IDF Diabetes Atlas. Retrieved from http://www.idf.org/sites/default/files/Atlas-poster-2014_EN.pdf Accessed 4 April 2014
Vos T. et al.: “Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010”. Lancet 380(9859), 2163–2196 (2013)
Diabetes Fact sheet \({{\rm N}^{\circ}312}\) . WHO. Retrieved from: http://www.who.int/mediacentre/factsheets/fs312/en/ Accessed: 4 April (2014)
Ministry of Health Oman Diabetes Mellitus: Management Guidelines for Primary Health Care. 2nd edn. Retrieved from: http://www.moh.gov.om/en/mgl/Manual/diabetesmoh.pdf Accessed: Aug (2013)
Sherimon P.C. et al.: Ontology driven analysis and prediction of patient risk in diabetes. Can. J. Pure Appl. Sci. 8(3), 3043–3050 (2014)
Buchanan, B.G., Edward, H.S. (eds.): Rule-based expert systems, vol. 3. Addison-Wesley, Reading, MA (1984)
Warner H.R. Jr, Bouhaddou O.: Innovation review: Iliad—a medical diagnostic support program. Top. Health Inf. Manag. 14(4), 51–58 (1994)
Kahn, C.E. Jr, et al.: Preliminary investigation of a Bayesian network for mammographic diagnosis of breast cancer. In: Proceedings of the annual symposium on computer application in medical care. American Medical Informatics Association (1995)
Miller, P.L. et al.: IMM/Serve: a rule-based program for childhood immunization. Proceedings of the AMIA Annual Fall Symposium. American Medical Informatics Association, (1996)
Abidi, S.R. et al.: Ontology-based modeling of clinical practice guidelines: a clinical decision support system for breast cancer follow-up interventions at primary care settings. In: Proceedings of the 12th world congress on health (Medical) informatics (Medinfo); Building sustainable health systems, pp. 845–849 (2007)
Ceccarelli, M.; Donatiello, A.; Vitale, D.: KON^ 3: a clinical decision support system, in oncology environment, based on knowledge management. Tools with artificial intelligence, 2008. In: 20th IEEE International Conference on ICTAI’08, vol. 2. IEEE (2008)
Bouamrane, M.-M.; Rector, A.; Hurrell, M.: Development of an ontology for a preoperative risk assessment clinical decision support system. In: Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems, pp. 1–6 (2009)
O’Connor P.J. et al.: Impact of electronic health record clinical decision support on diabetes care: a randomized trial. Ann. Fam. Med. 9(1), 12–21 (2011)
Sanchez, E. et al.: A knowledge-based clinical decision support system for the diagnosis of Alzheimer disease. In: e-Health Networking Applications and Services (Healthcom), 2011 13th IEEE International Conference on IEEE (2011)
Mahmud, F.B.; Yusof, M.M.; Shahrul, A.N.: Ontological based clinical decision support system (CDSS) for weaning ventilator in intensive care unit (ICU). In: Electrical Engineering and Informatics (ICEEI), 2011 International Conference on IEEE (2011)
Farmer N., Schilstra M.J.: A knowledge-based diagnostic clinical decision support system for musculoskeletal disorders of the shoulder for use in a primary care setting. Shoulder Elbow 4(2), 141–151 (2012)
Wang H.-T., Tansel A.U.: Composite ontology-based medical diagnosis decision support system framework. Commun. IIMA 13(2), 43 (2013)
Kumar, M.; Sharma, A.; Agarwal, S.: Clinical decision support system for diabetes disease diagnosis using optimized neural network. In: Engineering and Systems (SCES), 2014 Students Conference on IEEE (2014)
Zhao, T.: An ontology-based decision support system for interventions based on monitoring medical conditions on patients in hospital wards. Master Thesis in Information and Communication Technology (2014)
Assad, A.: Identifying design issues related the knowledge bases of medical decision support systems. Advanced Level Student Thesis (2010)
Lohmann, S.; Negru, S.; Bold, D.: The ProtégéVOWL plugin: ontology visualization for everyone. In: The Semantic Web: ESWC 2014 Satellite Events, pp. 395–400. Springer International Publishing (2014)
National Collaborating Centre for Chronic Conditions (UK. Type 2 Diabetes: National Clinical Guideline for Management in Primary and Secondary Care (update). Royal College of Physicians (UK) (2008)
Joseph, C.G.; Gary D.R.: Expert Systems: Principles and Programming, 4th edn. Course Technology, Boston, USA (2004)
Rattanasawad, T. et al.: A review and comparison of rule languages and rule-based inference engines for the Semantic Web. In: Computer Science and Engineering Conference (ICSEC), 2013 International. IEEE (2013)
Glimm, B. et al.: A Syntax for Rules in OWL 2. In: Experiences and Directions (OWLED), Sixth International Workshop on Chantilly, Virginia, USA, 23–24 October 2009
Kawazoe Y., Ohe K.: An ontology-based mediator of clinical information for decision support systems. Methods Inf. Med. 47(6), 549–559 (2008)
Risk Scoring Systems. Retrieved from http://www.framinghamheartstudy.org/. Accessed 4 April 2014
Open World Assumption. Retrieved from http://en.wikipedia.org/wiki/Open-world_assumption. Accessed 4 April (2014)
Wu R., Peters W., Morgan M.W.: The next generation of clinical decision support: linking evidence to best practice. J. Healthc. Inf. Manag.: JHIM 16(4), 50–55 (2001)
Lin F. et al.: Mining time dependency patterns in clinical pathways. Int. J. Med. Inform. 62(1), 11–25 (2001)
Polese G.: A decision support system for evidence based medicine. J. Vis. Lang. Comput. 25(6), 858–867 (2014)
Roeder N. et al.: Clinical pathways: effective and efficient inpatient treatment. Der Chirurg; Zeitschrift für alle Gebiete der operativen Medizen 74(12), 1149 (2003)
Craig J.C., Irwig L.M., Stockler M.R.: Evidence-based medicine: useful tools for decision making. Med. J. Aust. 174(5), 248–253 (2001)
Abidi, S.S.R.; Abidi, S.R.: A case for supplementing evidence based medicine with inductive clinical knowledge: towards a technology-enriched integrated clinical evidence system. In: Computer-Based Medical Systems, 2001. CBMS 2001. Proceedings. 14th IEEE Symposium on IEEE, (2001)
Gago P., Santos M.F.: Adaptive knowledge discovery for decision support in intensive care units. WSEAS Trans. Comput. 8(7), 1103–1112 (2009)
Castaneda C. et al.: Clinical decision support systems for improving diagnostic accuracy and achieving precision medicine. J. Clin. Bioinform. 5(1), 4 (2015)
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Sherimon, P.C., Krishnan, R. OntoDiabetic: An Ontology-Based Clinical Decision Support System for Diabetic Patients. Arab J Sci Eng 41, 1145–1160 (2016). https://doi.org/10.1007/s13369-015-1959-4
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DOI: https://doi.org/10.1007/s13369-015-1959-4