K I Mohammed1, A A Zaidan1, B B Zaidan2, O S Albahri1, A S Albahri1, M A Alsalem1, A H Mohsin1. 1. Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Malaysia. 2. Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Malaysia. Electronic address: bilalbahaa@fskik.upsi.edu.my.
Abstract
CONTEXT: Telemedicine has been increasingly used in healthcare to provide services to patients remotely. However, prioritising patients with multiple chronic diseases (MCDs) in telemedicine environment is challenging because it includes decision-making (DM) with regard to the emergency degree of each chronic disease for every patient. OBJECTIVE: This paper proposes a novel technique for reorganisation of opinion order to interval levels (TROOIL) to prioritise the patients with MCDs in real-time remote health-monitoring system. METHODS: The proposed TROOIL technique comprises six steps for prioritisation of patients with MCDs: (1) conversion of actual data into intervals; (2) rule generation; (3) rule ordering; (4) expert rule validation; (5) data reorganisation; and (6) criteria weighting and ranking alternatives within each rule. The secondary dataset of 500 patients from the most relevant study in a remote prioritisation area was adopted. The dataset contains three diseases, namely, chronic heart disease, high blood pressure (BP) and low BP. RESULTS: The proposed TROOIL is an effective technique for prioritising patients with MCDs. In the objective validation, remarkable differences were recognised among the groups' scores, indicating identical ranking results. In the evaluation of issues within all scenarios, the proposed framework has an advantage of 22.95% over the benchmark framework. DISCUSSION: Patients with the most severe MCD were treated first on the basis of their highest priority levels. The treatment for patients with less severe cases was delayed more than that for other patients. CONCLUSIONS: The proposed TROOIL technique can deal with multiple DM problems in prioritisation of patients with MCDs.
CONTEXT: Telemedicine has been increasingly used in healthcare to provide services to patients remotely. However, prioritising patients with multiple chronic diseases (MCDs) in telemedicine environment is challenging because it includes decision-making (DM) with regard to the emergency degree of each chronic disease for every patient. OBJECTIVE: This paper proposes a novel technique for reorganisation of opinion order to interval levels (TROOIL) to prioritise the patients with MCDs in real-time remote health-monitoring system. METHODS: The proposed TROOIL technique comprises six steps for prioritisation of patients with MCDs: (1) conversion of actual data into intervals; (2) rule generation; (3) rule ordering; (4) expert rule validation; (5) data reorganisation; and (6) criteria weighting and ranking alternatives within each rule. The secondary dataset of 500 patients from the most relevant study in a remote prioritisation area was adopted. The dataset contains three diseases, namely, chronic heart disease, high blood pressure (BP) and low BP. RESULTS: The proposed TROOIL is an effective technique for prioritising patients with MCDs. In the objective validation, remarkable differences were recognised among the groups' scores, indicating identical ranking results. In the evaluation of issues within all scenarios, the proposed framework has an advantage of 22.95% over the benchmark framework. DISCUSSION: Patients with the most severe MCD were treated first on the basis of their highest priority levels. The treatment for patients with less severe cases was delayed more than that for other patients. CONCLUSIONS: The proposed TROOIL technique can deal with multiple DM problems in prioritisation of patients with MCDs.
Authors: M A Alsalem; O S Albahri; A A Zaidan; Jameel R Al-Obaidi; Alhamzah Alnoor; A H Alamoodi; A S Albahri; B B Zaidan; F M Jumaah Journal: Appl Intell (Dordr) Date: 2022-01-08 Impact factor: 5.019
Authors: O S Albahri; A A Zaidan; A S Albahri; H A Alsattar; Rawia Mohammed; Uwe Aickelin; Gang Kou; F M Jumaah; Mahmood M Salih; A H Alamoodi; B B Zaidan; Mamoun Alazab; Alhamzah Alnoor; Jameel R Al-Obaidi Journal: J Adv Res Date: 2021-08-21 Impact factor: 12.822
Authors: A H Mohsin; A A Zaidan; B B Zaidan; K I Mohammed; O S Albahri; A S Albahri; M A Alsalem Journal: Multimed Tools Appl Date: 2021-01-22 Impact factor: 2.757
Authors: Armin Yazdani; Kasturi Dewi Varathan; Yin Kia Chiam; Asad Waqar Malik; Wan Azman Wan Ahmad Journal: BMC Med Inform Decis Mak Date: 2021-06-21 Impact factor: 2.796