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Intelligent Emotion and Sensory Remote Prioritisation for Patients with Multiple Chronic Diseases.

A H Alamoodi1, O S Albahri2, A A Zaidan3

  • 1Faculty of Computing and Meta-Technology (FKMT), Universiti Pendidikan Sultan Idris (UPSI), Tanjong Malim 35900, Perak, Malaysia.

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Summary

This study introduces intelligent remote patient prioritization for those with multiple chronic diseases, using emotion and sensory data. It prioritizes patients with severe low blood pressure, enhancing care for high-risk individuals.

Keywords:
emotion criteriamulti-chronic diseasesmulti-criteria decision makingpatients prioritisationsensor criteria

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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Healthcare
  • Decision Science

Background:

  • Managing patients with multiple high-risk chronic diseases presents complex prioritization challenges.
  • Existing remote monitoring systems may not adequately integrate diverse patient data for effective risk stratification.
  • The need for intelligent systems to optimize care for vulnerable patient populations is growing.

Purpose of the Study:

  • To propose an intelligent remote prioritization system for patients with multiple high-risk chronic diseases.
  • To integrate emotion and sensory measurements with multi-criteria decision-making for enhanced patient assessment.
  • To develop a modified TROOIL-FWZIC method for objective weighting in fuzzy decision matrices.

Main Methods:

  • A two-phase methodology involving a case study with a multi-criteria decision matrix.
  • Modification of the Technique for Reorganizing Opinion Order to Interval Levels (TROOIL) using an extended Fuzzy-Weighted Zero-Inconsistency (FWZIC) method.
  • Application of fractional orthotriple fuzzy sets to address objective weighting challenges.

Main Results:

  • Chronic heart disease identified as the primary criterion, followed by emotion-based factors.
  • Sensor-based criterion 'Peaks' and emotion criterion 'chest pain' highlighted in the third hierarchy.
  • Low blood pressure disease identified as the most critical criterion for prioritizing severe cases.

Conclusions:

  • The proposed intelligent system effectively prioritizes high-risk patients based on integrated multi-criteria data.
  • The modified TROOIL-FWZIC method offers improved objective weighting for fuzzy decision-making.
  • This approach enhances remote patient management and care allocation for chronic disease patients.