A Fuzzy-Based Customisation of Healthcare Knowledge to Support Clinical Domestic Decisions for Chronically Ill Patients

Firas Zekri, Afef Samet Ellouze () and Rafik Bouaziz
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Firas Zekri: Mir@cl Laboratory, University of Sfax, Sfax, Tunisia†Faculty of Economics and Management, Sfax University, Tunisia
Afef Samet Ellouze: #x2021;Higher Institute of Technological Studies, Sfax, Tunisia
Rafik Bouaziz: Mir@cl Laboratory, University of Sfax, Sfax, Tunisia†Faculty of Economics and Management, Sfax University, Tunisia

Journal of Information & Knowledge Management (JIKM), 2020, vol. 19, issue 04, 1-28

Abstract: The development of customised healthcare systems is becoming an important issue in the healthcare industry due to the rapid increase in the number of chronically ill patients. These systems aim to deliver effective care to patients having chronic diseases through customised services. However, knowledge bases need also to be customised since systems are confronted with huge amount of personalised and imprecise medical knowledge. Therefore, we propose in this paper a new system to customise medical knowledge according to progressive disease phases and pathological cases. A rule management process first customises rules according to the specificities of every disease phase, and then matches a private knowledge base with each enrolled patient. This base contains only the patient’s customised knowledge. After reasoning, another customisation process is carried out by the component, Result Manager, which ensures the validation of the system outcomes by the pathological case experts, before being recommended. This will better ensure the recommendation of the generated results to the non-professional users. In addition, Result Manager offers fuzzy semantic queries to the experts. In conclusion, our new decision support system makes medical aid decisions not only addressed to physicians, but also to chronically ill patients and persons regarded as caregivers.

Keywords: Clinical decision support system; expert system; knowledge customisation; fuzzy logic; fuzzy ontology (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1142/S021964922050029X

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