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    Artificial Intelligence in Healthcare Industry: A Transformation From Model-Driven to Knowledge-Driven DSS
    (2022) Hızıroğlu, Abdulkadir; Pişirgen, Ali; Özcan, Mert; İlter, Halil Kemal
    Healthcare professionals and inter (or multi) disciplinary academia have been paying more attention to decision support systems (DSS) for improved decision making during their health service processes or management, as well as clinical practices. Although there have been numerous DSS applications in the healthcare industry, it has been intended to provide a categorical snapshot view of current implementations or academic work at specific DSS types for better understanding the application domains by addressing the gap in the literature. To achieve this, it has been focused on DSS applications in healthcare specifically by concentrating on two main types: model-driven and knowledge-driven. In this context, relevant information systems and medical science literatures were reviewed. For health service problems like hospital placement decisions and homecare route planning, model-driven DSS applications are used for optimization and modelling. Both conventional operations research techniques like optimization, decision analysis, simulation, and multi-criteria decision making, as well as contemporary ones like heuristic search, benefit from these applications. In addition, artificial intelligence techniques help health decision makers via knowledge-driven DSS applications, specifically clinical decision support systems (CDSS). Artificial intelligence applications can also assist health professionals in enhancing their decision-making abilities by incorporating complex operational rules and developing such procedures as single-or multi-agent systems. This research focuses on what to emphasis on while designing a DSS in the healthcare setting, such as which programming or modelling languages to employ and how to transform a model-driven DSS into a knowledgedriven DSS, or how to create the DSS more intelligent. Overall, this study indicates a present course for DSS and offers useful knowledge for both scholars and professionals in the healthcare domain.
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    Öğe
    Designing a data warehouse for earthquake risk assessment of buildings: A case study for healthcare facilities
    (2021) Özcan, Mert; Peker, Serhat
    Since earthquake is one of the most dangerous natural phenomena, it is necessary to be prepared for the negative consequences of the earthquake in advance. It is very important that healthcare facilities must continue to provide service during and after an earthquake. Therefore, this study focuses on designing a data warehouse model for the earthquake risk assessment of healthcare facilities which are needed much more than other public buildings phys-ically. The proposed design utilizes a fact constellation schema model and take a public legislation containing principles regarding identification of risky buildings. This solution can provide a repository for data regarding earthquake risk assessment from different operational systems and play a key role in supporting critical decision-making process.