在SARS病例中识别模式:从t-SNE和k-means集群应用到COVID-19症状学中的见解
Julliana Gonçalves Marques1, Bruno Motta de Carvalho1, Luiz Affonso Guedes2
1Department of Informatics and Applied Mathematics, Federal University of Rio Grande do Norte, Natal, Brazil.
Frontiers in artificial intelligence
|April 11, 2025
概括
COVID-19和SARS有类似的症状,使诊断复杂化. 这项研究使用t-SNE和聚类来分析SARS-CoV-2和未指定的SARS病例中的症状模式,发现了显著的重叠.
科学领域:
- 流行病学 流行病学
- 数据科学数据科学数据科学
- 医学诊断 医学诊断 医学诊断
背景情况:
- 由于症状与其他SARS菌株重叠,COVID-19大流行突出了诊断挑战.
- 长期的COVID和类似的症状使病毒性呼吸道疾病的准确和及时诊断变得复杂.
研究的目的:
- 通过使用缩小维度和集群来视觉分析SARS感染个体之间的结构相似性.
- 为了确定病例进展和诊断是否影响SARS-CoV-2和未指定的SARS病例的症状模式.
主要方法:
- 使用t-分布式静态邻居嵌入 (t-SNE) 进行维度缩小.
- 用Gower的距离用于分类数据和k-means集群.
- 分析了来自巴西SARS数据集 (2020-2021) 的症状数据,病例进展和诊断.
主要成果:
- 在COVID-19和未指定的SARS诊断之间确定了大量的症状相似性.
- 观察到共享的症状模式,即使大多数人被诊断为未指定的SARS.
- 突出了错误诊断的可能性,因为临床表现重叠.
结论:
- 这项研究强调了不同SARS菌株中类似症状引起的诊断复杂性.
- 这些发现对于了解大流行病对巴西的影响至关重要,巴西是受严重影响的国家.
- 建议需要精细的诊断策略,考虑到症状重叠.
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