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An Intuitionistic Multiplicative ORESTE Method for Patients’ Prioritization of Hospitalization

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  • Cheng Zhang

    (Business School, Sichuan University, Chengdu 610064, China)

  • Xingli Wu

    (Business School, Sichuan University, Chengdu 610064, China)

  • Di Wu

    (Business School, Sichuan University, Chengdu 610064, China)

  • Huchang Liao

    (Business School, Sichuan University, Chengdu 610064, China
    Department of Computer Science and Artificial Intelligence, University of Granada, E-18071 Granada, Spain)

  • Li Luo

    (Business School, Sichuan University, Chengdu 610064, China)

  • Enrique Herrera-Viedma

    (Department of Computer Science and Artificial Intelligence, University of Granada, E-18071 Granada, Spain
    Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

Abstract

The tension brought about by sickbeds is a common and intractable issue in public hospitals in China due to the large population. Assigning the order of hospitalization of patients is difficult because of complex patient information such as disease type, emergency degree, and severity. It is critical to rank the patients taking full account of various factors. However, most of the evaluation criteria for hospitalization are qualitative, and the classical ranking method cannot derive the detailed relations between patients based on these criteria. Motivated by this, a comprehensive multiple criteria decision making method named the intuitionistic multiplicative ORESTE (organísation, rangement et Synthèse dedonnées relarionnelles, in French) was proposed to handle the problem. The subjective and objective weights of criteria were considered in the proposed method. To do so, first, considering the vagueness of human perceptions towards the alternatives, an intuitionistic multiplicative preference relation model is applied to represent the experts’ preferences over the pairwise alternatives with respect to the predetermined criteria. Then, a correlation coefficient-based weight determining method is developed to derive the objective weights of criteria. This method can overcome the biased results caused by highly-related criteria. Afterwards, we improved the general ranking method, ORESTE, by introducing a new score function which considers both the subjective and objective weights of criteria. An intuitionistic multiplicative ORESTE method was then developed and further highlighted by a case study concerning the patients’ prioritization.

Suggested Citation

  • Cheng Zhang & Xingli Wu & Di Wu & Huchang Liao & Li Luo & Enrique Herrera-Viedma, 2018. "An Intuitionistic Multiplicative ORESTE Method for Patients’ Prioritization of Hospitalization," IJERPH, MDPI, vol. 15(4), pages 1-18, April.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:4:p:777-:d:141603
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    References listed on IDEAS

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    1. Esra AYTAC ADALI & Aysegul TUS ISIK, 2017. "Ranking Web Design Firms with the ORESTE Method," Ege Academic Review, Ege University Faculty of Economics and Administrative Sciences, vol. 17(2), pages 243-254.
    2. Yancang Li & Lei Zhao & Juanjuan Suo, 2014. "Comprehensive Assessment on Sustainable Development of Highway Transportation Capacity Based on Entropy Weight and TOPSIS," Sustainability, MDPI, vol. 6(7), pages 1-9, July.
    3. Roubens, Marc, 1982. "Preference relations on actions and criteria in multicriteria decision making," European Journal of Operational Research, Elsevier, vol. 10(1), pages 51-55, May.
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