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AI-Driven decision-making for personalized elderly care: a fuzzy MCDM-based framework for enhancing treatment
1Department of Computer Science, Applied College, Taibah University, 42353, Medina, Saudi Arabia. aahjohani@taibahu.edu.sa.
This study introduces an automated system using AI, Fuzzy VIKOR, and Electronic Health Records (EHR) to provide personalized healthcare recommendations for the elderly, enhancing treatment selection and sustainability.
Area of Science:
- Geriatric Medicine
- Health Informatics
- Decision Science
Background:
- Aging populations present significant challenges to global healthcare systems.
- There is a critical need for sustainable and effective healthcare solutions for seniors.
- Personalized healthcare recommendations are vital for improving patient outcomes and facility sustainability.
Purpose of the Study:
- To develop an automated decision-making mechanism for personalized healthcare recommendations for the elderly.
- To integrate automation, Fuzzy VIKOR, and Electronic Health Record (EHR) data for improved treatment efficacy and accuracy.
- To bridge the gap between automated intelligence and patient-centered care in geriatric healthcare.
Main Methods:
- Utilized Electronic Health Record (EHR) data for comprehensive clinical insights.
- Employed Artificial Intelligence (AI) techniques for enhanced data processing.
- Applied the Fuzzy VIKOR approach to manage decision-making uncertainty and patient preferences.
Main Results:
- The proposed methodology refines therapy selection for the senior population.
- A personalized approach provides ranked treatment alternatives based on individual patient characteristics.
- Demonstrated the potential to manage healthcare complexity and advance precision medicine.
Conclusions:
- The integration of AI, Fuzzy VIKOR, and EHR data offers a promising strategy for sustainable elderly healthcare.
- Personalized recommendations improve patient outcomes and the long-term viability of healthcare services.
- This approach prioritizes individual patient needs and preferences in treatment decision-making.
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