EDAS method for decision support modeling under the Pythagorean probabilistic hesitant fuzzy aggregation information.

Area of Science:

• Decision Sciences
• Public Health
• Computational Intelligence

Background:

• Emergency decision-making (EmDM) is critical for mitigating losses during crises.
• Real-world EmDM faces challenges like complexity, time constraints, data scarcity, and psychological factors.
• Selecting effective treatments for diseases like coronavirus presents a significant clinical analysis challenge.

Purpose of the Study:

• To develop a robust multi-attribute decision-making approach (MADMap) for drug selection in emergency situations.
• To address uncertainty in clinical analysis and EmDM using Pythagorean probabilistic hesitant fuzzy information.
• To provide a practical algorithm for selecting optimal drugs for coronavirus disease.

Main Methods:

• A multi-attribute decision-making approach (MADMap) based on the EDAS method.
• Utilizing Pythagorean probabilistic hesitant fuzzy information to handle uncertainty.
• Development of a specific algorithm for drug selection in clinical emergency contexts.

Main Results:

• The proposed MADMap effectively handles uncertainty in drug selection for coronavirus treatment.
• A case study demonstrates the practicality and feasibility of the developed approach.
• Comparative analysis confirms the efficiency and applicability of the methodology over existing techniques like TOPSIS.

Conclusions:

• The developed MADMap provides an efficient and applicable solution for drug selection in emergency clinical analysis.
• This approach enhances emergency decision-making (EmDM) by systematically managing complex attributes and uncertainties.
• The study offers a valuable tool for optimizing treatment strategies during public health emergencies.