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A case-based reasoning framework for early detection and diagnosis of novel coronavirus

机译：基于案例的早期检测和诊断冠状病病毒的推理框架

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Coronavirus, also known as COVID-19, has been declared a pandemic by the World Health Organization (WHO). At the time of conducting this study, it had recorded over 11,301,850 confirmed cases while more than 531,806 have died due to it, with these figures rising daily across the globe. The burden of this highly contagious respiratory disease is that it presents itself in both symptomatic and asymptomatic patterns in those already infected, thereby leading to an exponential rise in the number of contractions of the disease and fatalities. It is, therefore, crucial to expedite the process of early detection and diagnosis of the disease across the world. The case-based reasoning (CBR) model is a compelling paradigm that allows for the utilization of case-specific knowledge previously experienced, concrete problem situations or specific patient cases for solving new cases. This study, therefore, aims to leverage the very rich database of cases of COVID-19 to solve new cases. The approach adopted in this study employs the use of an improved CBR model for state-of-the-art reasoning task in the classification of suspected cases of COVID-19. The CBR model leverages on a novel feature selection and the semantic-based mathematical model proposed in this study for case similarity computation. An initial population of the archive was achieved from 71 (67 adults and 4 pediatrics) cases obtained from the Italian Society of Medical and Interventional Radiology (SIRM) repository. Results obtained revealed that the proposed approach in this study successfully classified suspected cases into their categories with an accuracy of 94.54%. The study found that the proposed model can support physicians to easily diagnose suspected cases of COVID-19 based on their medical records without subjecting the specimen to laboratory tests. As a result, there will be a global minimization of contagion rate occasioned by slow testing and in addition, reduced false-positive rates of diagnosed cases as observed in some parts of the globe.
机译：冠状病毒，又称Covid-19，已被世界卫生组织（世卫组织）宣布为大流行。在进行这项研究时，它录得超过11,301,850个确认的病例，而在全球每天上升，这些数字因其死亡超过531,806。这种高度传染性呼吸道疾病的负担是它在已经感染的人中呈现出症状和无症状模式，从而导致疾病和死亡的收缩次数的指数上升。因此，加快全世界疾病的早期检测和诊断过程至关重要。基于案例的推理（CBR）模型是一个令人尖锐的范例，允许利用先前经历过的具体知识，具体的问题情况或特定患者病例来解决新病例。因此，这项研究旨在利用Covid-19案件的丰富数据库来解决新案例。本研究采用的方法采用改进的CBR模型，以便在Covid-19的疑似病例的分类中进行最先进的推理任务。 CBR模型利用了本研究中提出的新颖特征选择和基于语义的数学模型，以实现案例相似性计算。归档的初始群体是从意大利医学和介入放射学（特征）的意大利医学和介入的储存库中获得的71名（67名成人和4个儿科）案件。获得的结果表明，本研究中提出的方法将疑似案件成功地归入其类别，精度为94.54％。该研究发现，拟议的模型可以支持医生，以便根据其医疗记录轻松诊断有疑似的Covid-19病例，而无需对实验室测试进行标本。结果，通过缓慢的测试，将存在全球最小化传染率，并且此外，减少了在全球某些地区观察到的诊断病例​​的假阳性率。

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