用于抗击新冠疫情的机器学习综合综述
A Comprehensive Review of Machine Learning Used to Combat COVID-19.
作者信息
Gomes Rahul, Kamrowski Connor, Langlois Jordan, Rozario Papia, Dircks Ian, Grottodden Keegan, Martinez Matthew, Tee Wei Zhong, Sargeant Kyle, LaFleur Corbin, Haley Mitchell
机构信息
Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA.
Department of Geography and Anthropology, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA.
出版信息
Diagnostics (Basel). 2022 Jul 31;12(8):1853. doi: 10.3390/diagnostics12081853.
Coronavirus disease (COVID-19) has had a significant impact on global health since the start of the pandemic in 2019. As of June 2022, over 539 million cases have been confirmed worldwide with over 6.3 million deaths as a result. Artificial Intelligence (AI) solutions such as machine learning and deep learning have played a major part in this pandemic for the diagnosis and treatment of COVID-19. In this research, we review these modern tools deployed to solve a variety of complex problems. We explore research that focused on analyzing medical images using AI models for identification, classification, and tissue segmentation of the disease. We also explore prognostic models that were developed to predict health outcomes and optimize the allocation of scarce medical resources. Longitudinal studies were conducted to better understand COVID-19 and its effects on patients over a period of time. This comprehensive review of the different AI methods and modeling efforts will shed light on the role that AI has played and what path it intends to take in the fight against COVID-19.
自2019年疫情开始以来，冠状病毒病（COVID-19）对全球健康产生了重大影响。截至2022年6月，全球确诊病例超过5.39亿例，导致超过630万人死亡。机器学习和深度学习等人工智能（AI）解决方案在这场疫情中对COVID-19的诊断和治疗发挥了重要作用。在本研究中，我们回顾了为解决各种复杂问题而部署的这些现代工具。我们探讨了专注于使用人工智能模型分析医学图像以识别、分类和分割疾病组织的研究。我们还探讨了为预测健康结果和优化稀缺医疗资源分配而开发的预后模型。进行了纵向研究，以更好地了解COVID-19及其在一段时间内对患者的影响。对不同人工智能方法和建模工作的全面回顾将揭示人工智能在抗击COVID-19中所发挥的作用以及它打算采取的路径。
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