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

Abstract

Context and background: Corona virus (COVID) has rapidly gained a foothold and caused a global pandemic. Particularists try their best to tackle this global crisis. New challenges outlined from various medical perspectives may require a novel design solution. Asymptomatic COVID-19 carriers show different health conditions and no symptoms; hence, a differentiation process is required to avert the risk of chronic virus carriers.

Objectives: Laboratory criteria and patient dataset are compulsory in constructing a new framework. Prioritisation is a popular topic and a complex issue for patients with COVID-19, especially for asymptomatic carriers due to multi-laboratory criteria, criterion importance and trade-off amongst these criteria. This study presents new integrated decision-making framework that handles the prioritisation of patients with COVID-19 and can detect the health conditions of asymptomatic carriers.

Methods: The methodology includes four phases. Firstly, eight important laboratory criteria are chosen using two feature selection approaches. Real and simulation datasets from various medical perspectives are integrated to produce a new dataset involving 56 patients with different health conditions and can be used to check asymptomatic cases that can be detected within the prioritisation configuration. The first phase aims to develop a new decision matrix depending on the intersection between 'multi-laboratory criteria' and 'COVID-19 patient list'. In the second phase, entropy is utilised to set the objective weight, and TOPSIS is adapted to prioritise patients in the third phase. Finally, objective validation is performed.

Results: The patients are prioritised based on the selected criteria in descending order of health situation starting from the worst to the best. The proposed framework can discriminate among mild, serious and critical conditions and put patients in a queue while considering asymptomatic carriers. Validation findings revealed that the patients are classified into four equal groups and showed significant differences in their scores, indicating the validity of ranking.

Conclusions: This study implies and discusses the numerous benefits of the suggested framework in detecting/recognising the health condition of patients prior to discharge, supporting the hospitalisation characteristics, managing patient care and optimising clinical prediction rule.

Keywords: COVID-19; Entropy; Laboratory characteristics; Multi-criteria decision making; Prioritisation; Respiratory; TOPSIS.

Fig. 1

Methodology phases.

Fig. 2

The obtained results from three automatic feature selection algorithms.

Fig. 3

Flowchart of integrated Entropy-TOPSIS methods.

Fig. 4

Multi-laboratory characteristics weights chart based on Entropy.

Fig. 5

Results of four groups of patients. (A)1^stgroup.(B) 2^ndgroup.(C) 3^rdgroup.(D)4^thgroup.

Fig. 6

Bar Charts for Mean Results for Four Groups of Patients.