Based on Real Time Remote Health Monitoring Systems: A New Approach for Prioritization “Large Scales Data” Patients with Chronic Heart Diseases Using Body Sensors and Communication Technology

Abstract

This paper presents a new approach to prioritize "Large-scale Data" of patients with chronic heart diseases by using body sensors and communication technology during disasters and peak seasons. An evaluation matrix is used for emergency evaluation and large-scale data scoring of patients with chronic heart diseases in telemedicine environment. However, one major problem in the emergency evaluation of these patients is establishing a reasonable threshold for patients with the most and least critical conditions. This threshold can be used to detect the highest and lowest priority levels when all the scores of patients are identical during disasters and peak seasons. A practical study was performed on 500 patients with chronic heart diseases and different symptoms, and their emergency levels were evaluated based on four main measurements: electrocardiogram, oxygen saturation sensor, blood pressure monitoring, and non-sensory measurement tool, namely, text frame. Data alignment was conducted for the raw data and decision-making matrix by converting each extracted feature into an integer. This integer represents their state in the triage level based on medical guidelines to determine the features from different sources in a platform. The patients were then scored based on a decision matrix by using multi-criteria decision-making techniques, namely, integrated multi-layer for analytic hierarchy process (MLAHP) and technique for order performance by similarity to ideal solution (TOPSIS). For subjective validation, cardiologists were consulted to confirm the ranking results. For objective validation, mean ± standard deviation was computed to check the accuracy of the systematic ranking. This study provides scenarios and checklist benchmarking to evaluate the proposed and existing prioritization methods. Experimental results revealed the following. (1) The integration of TOPSIS and MLAHP effectively and systematically solved the patient settings on triage and prioritization problems. (2) In subjective validation, the first five patients assigned to the doctors were the most urgent cases that required the highest priority, whereas the last five patients were the least urgent cases and were given the lowest priority. In objective validation, scores significantly differed between the groups, indicating that the ranking results were identical. (3) For the first, second, and third scenarios, the proposed method exhibited an advantage over the benchmark method with percentages of 40%, 60%, and 100%, respectively. In conclusion, patients with the most and least urgent cases received the highest and lowest priority levels, respectively.

Detection-based prioritisation: Framework of multi-laboratory characteristics for asymptomatic COVID-19 carriers based on integrated Entropy–TOPSIS methods

“…The results are validated by employing the objective validation in accordance with previously described methods presented in [ 78 , 79 ]. The following steps were conducted to ensure that the results were statistically ranked: Final prioritisation results are categorised into four equal groups.…”

Multi-Biological Laboratory Examination Framework for the Prioritization of Patients with COVID-19 Based on Integrated AHP and Group VIKOR Methods

“…The development methodology of the proposed prioritization framework is divided into three sequence phases (i.e., identi¯cation, setting weights for biological laboratory COVID-19 criteria and prioritization of patients with COVID- 19). Figure 1 shows the structure of the research methodology phases.…”

“…Previous studies on GDM suggested two common con¯gurations, that is, internal aggregation or external aggregation, 86 which were applied for several medical domains. [87][88][89] Internal aggregation aims to aggregate the obtained weights from each expert and compute the average weights for each criterion. In the present study, internal aggregation was used to combine the weights extracted from each expert by applied arithmetic mean, and the VIKOR method was applied for prioritization steps.…”

“…Initially, the virus was named 2019 novel coronavirus and has now been named SARS-CoV-2 by the International Committee of Taxonomy of Viruses. 96 This virus can cause a disease called coronavirus disease 2019 (COVID- 19). The World Health Organization refers to the virus as COVID-19 virus in its reports.…”

“…[13][14][15][16] Therefore, patient prioritization primarily aims to identify patients who can safely wait and those who cannot. [17][18][19][20][21][22] Patient condition should be the primary tool in assessing the priority in accordance with medical guidelines. [24][25][26][27] In a rapidly spreading disease situations as in COVID-19, di®erentiating mild from serious health cases is a di±cult but essential task when providing healthcare services by medical sectors.…”