机器学习和COVID-19:来自SARS-CoV-2的教训
Ugo Avila-Ponce de León1,2, Aarón Vazquez-Jimenez2, Alejandra Cervera3
1Programa de Doctorado en Ciencias Biológicas, Universidad Nacional Autónoma de México, Ciudad de México, Mexico.
Advances in experimental medicine and biology
|June 28, 2023
概括
机器学习技术有助于预测COVID-19患者的结果,并识别患者群体以改善分拣. 这些方法与系统生物学相结合,将关联研究与机械学框架联系起来,以获得更好的公共卫生洞察力.
科学领域:
- 计算生物学是一种计算生物学.
- 流行病学 流行病学
- 医疗信息学 医疗信息学
背景情况:
- 机器学习 (ML) 为分析大型数据集提供了强大的工具.
- 在应对包括COVID-19流行病在内的健康挑战方面,ML应用至关重要.
- 监督和无监督的ML技术可以识别健康数据中的模式.
研究的目的:
- 为COVID-19数据提供监督和无监督的ML技术.
- 为了证明ML对卫生当局在管理大流行病方面的贡献.
- 讨论ML在处理与COVID-19相关的社会行为和高通量数据方面的实际应用.
主要方法:
- 使用监督和无监督的机器学习算法.
- 开发用于预测COVID-19患者严重程度 (严重,中度,无症状) 的分类器.
- 将ML应用于高吞吐量和临床数据,以及系统生物学方法.
主要成果:
- 识别用于预测COVID-19患者反应的强大分类器.
- 将具有相似生理反应的患者分组为增强的分拣和治疗.
- 通过机器学习和系统生物学,将关联研究与机械学框架联系起来.
结论:
- 机器学习为了解和管理COVID-19大流行提供了关键工具.
- ML通过识别患者子组和预测疾病严重程度,有助于个性化医疗.
- 将ML与系统生物学相结合,为深入了解疾病机制和进化提供了一条途径.
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