摘要
2019冠状病毒病(COVID-19)是由新发现的包膜病毒(SARS-CoV-2)引起的传染病,于2019年12月在中国武汉首次报告,并影响全球。感染者可出现高烧、咳嗽、肌痛、淋巴细胞减少、呼吸窘迫综合征等症状,或在2至14天潜伏期后完全无症状。由于病毒是通过吸入打喷嚏或咳嗽产生的具有传染性的呼吸道飞沫传播的,因此及早和迅速诊断该病可以防止感染和传播。在当前的大流行形势下,医疗行业正在寻找监测和控制COVID-19传播的新技术。在这种背景下,当前的综述文章强调了人工智能方法,通过模式识别、机器学习、专家系统和模糊逻辑,通过改善认知行为和减少人为错误,在快速、准确和早期诊断疾病方面发挥着有效作用。自动编码器深度学习方法、α-卫星、ACEMod和异质图自动编码器是确定病毒传播速率的人工智能方法,有助于塑造公共卫生和规划。此外,CT扫描、x射线、MRI和RT-PCR是正在被用于识别COVID-19的一些技术。我们希望使用人工智能技术;世界可以在缓解社会和经济危机的同时摆脱COVID-19大流行。
关键词: 人工智能,冠状病毒,机器学习,COVID-19,大流行,诊断
Current Medicinal Chemistry
Title:Machine Intelligence Techniques for the Identification and Diagnosis of COVID-19
Volume: 28 Issue: 26
关键词: 人工智能,冠状病毒,机器学习,COVID-19,大流行,诊断
摘要: COVID-19, an infectious disease caused by a newly discovered enveloped virus (SARS-CoV-2), was first reported in Wuhan, China, in December 2019 and affected the whole world. The infected individual may develop symptoms such as high fever, cough, myalgia, lymphopenia, respiratory distress syndrome etc., or remain completely asymptomatic after the incubation period of two to fourteen days. As the virus is transmitted by inhaling infectious respiratory droplets that are produced by sneezing or coughing, so early and rapid diagnosis of the disease can prevent infection and transmission. In the current pandemic situation, the medical industry is looking for new technologies to monitor and control the spread of COVID-19. In this context, the current review article highlights the Artificial Intelligence methods that are playing an effective role in rapid, accurate and early diagnosis of the disease via pattern recognition, machine learning, expert system and fuzzy logic by improving cognitive behavior and reducing human error. Auto-encoder deep learning method, α-satellite, ACEMod and heterogeneous graph auto- encoder are AI approaches that determine the transfer rate of virus and are helpful in shaping public health and planning. In addition, CT scan, X-ray, MRI, and RT-PCR are some of the techniques that are being employed in the identification of COVID-19. We hope using AI techniques; the world can emerge from COVID-19 pandemic while mitigating social and economic crisis.
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Cite this article as:
Machine Intelligence Techniques for the Identification and Diagnosis of COVID-19, Current Medicinal Chemistry 2021; 28 (26) . https://dx.doi.org/10.2174/0929867328666210106143307
DOI https://dx.doi.org/10.2174/0929867328666210106143307 |
Print ISSN 0929-8673 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-533X |
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