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Detection-based prioritisation: Framework of multi-laboratory characteristics for asymptomatic COVID-19 carriers based on integrated Entropy–TOPSIS methods
Artificial Intelligence in Medicine ( IF 7.5 ) Pub Date : 2020-11-07 , DOI: 10.1016/j.artmed.2020.101983
A S Albahri 1 , Rula A Hamid 2 , O S Albahri 3 , A A Zaidan 3
Affiliation  

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.



中文翻译:

基于检测的优先排序:基于综合熵-TOPSIS 方法的无症状 COVID-19 携带者的多实验室特征框架

上下文和背景

冠状病毒 (COVID) 已迅速站稳脚跟并引发全球大流行。特殊主义者竭尽全力应对这场全球危机。从各种医学角度概述的新挑战可能需要新颖的设计解决方案。无症状的 COVID-19 携带者表现出不同的健康状况并且没有症状;因此,需要一个分化过程来避免慢性病毒携带者的风险。

目标

实验室标准和患者数据集是构建新框架的必要条件。由于多实验室标准、标准重要性和这些标准之间的权衡,优先级排序是 COVID-19 患者的热门话题和复杂问题,尤其是对于无症状携带者。这项研究提出了新的综合决策框架,可以处理 COVID-19 患者的优先次序,并可以检测无症状携带者的健康状况。

方法

该方法包括四个阶段。首先,使用两种特征选择方法选择八个重要的实验室标准。来自不同医学角度的真实和模拟数据集被整合以产生一个新的数据集,涉及 56 名具有不同健康状况的患者,可用于检查在优先配置中可以检测到的无症状病例。第一阶段旨在根据“多实验室标准”和“COVID-19 患者名单”之间的交集开发一个新的决策矩阵。在第二阶段,利用熵来设置目标权重,并在第三阶段采用 TOPSIS 对患者进行优先排序。最后,进行客观验证。

结果

根据选定的标准,按照健康状况从最差到最好的降序排列患者的优先级。拟议的框架可以区分轻度、严重和危重情况,并在考虑无症状携带者的同时将患者排入队列。验证结果显示,患者被分为四个相等的组,并且他们的分数显示出显着差异,表明排名的有效性。

结论

本研究暗示并讨论了建议框架在出院前检测/识别患者健康状况、支持住院特征、管理患者护理和优化临床预测规则方面的众多好处。

更新日期:2020-11-19
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