An Intuitionistic Multiplicative ORESTE Method for Patients’ Prioritization of Hospitalization
Metadatos
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MDPI
Materia
Intuitionistic multiplicative preference relation ORESTE Multiple criteria decision making Correlation coefficient Patients’ prioritization Hospital management
Date
2018-04-17Referencia bibliográfica
Zhang, C. [et al.]. An Intuitionistic Multiplicative ORESTE Method for Patients’ Prioritization of Hospitalization. Int. J. Environ. Res. Public Health 2018, 15, 777.
Patrocinador
The work was supported by the National Natural Science Foundation of China (71501135, 71771156, 71532007), the Scientific Research Foundation for Excellent Young Scholars at Sichuan University (No. 2016SCU04A23), and the Grant from the FEDER funds (No. TIN2016-75850-R).Résumé
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.