Cargando…

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

An intelligent remote prioritization for patients with high-risk multiple chronic diseases is proposed in this research, based on emotion and sensory measurements and multi-criteria decision making. The methodology comprises two phases: (1) a case study is discussed through the adoption of a multi-c...

Descripción completa

Detalles Bibliográficos
Autores principales: Alamoodi, A. H., Albahri, O. S., Zaidan, A. A., Alsattar, H. A., Zaidan, B. B., Albahri, A. S., Ismail, Amelia Ritahani, Kou, Gang, Alzubaidi, Laith, Talal, Mohammed
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9959399/
https://www.ncbi.nlm.nih.gov/pubmed/36850457
http://dx.doi.org/10.3390/s23041854
Descripción
Sumario:An intelligent remote prioritization for patients with high-risk multiple chronic diseases is proposed in this research, based on emotion and sensory measurements and multi-criteria decision making. The methodology comprises two phases: (1) a case study is discussed through the adoption of a multi-criteria decision matrix for high-risk level patients; (2) the technique for reorganizing opinion order to interval levels (TROOIL) is modified by combining it with an extended fuzzy-weighted zero-inconsistency (FWZIC) method over fractional orthotriple fuzzy sets to address objective weighting issues associated with the original TROOIL. In the first hierarchy level, chronic heart disease is identified as the most important criterion, followed by emotion-based criteria in the second. The third hierarchy level shows that Peaks is identified as the most important sensor-based criterion and chest pain as the most important emotion criterion. Low blood pressure disease is identified as the most important criterion for patient prioritization, with the most severe cases being prioritized. The results are evaluated using systematic ranking and sensitivity analysis.