多模式生物医学数据融合使用稀有法定相关性分析和合作学习:关于COVID-19的队列研究
Ahmet Gorkem Er1,2,3, Daisy Yi Ding4, Berrin Er5
1Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University, Stanford, CA, 94305, USA.
Research square
|December 4, 2023
概括
使用稀疏的正规相关性分析 (CCA) 和合作学习分析COVID-19患者的多式数据,揭示了关键的临床表型,并改善了结果预测. 这些方法有效地处理复杂的生物医学数据,以获得强大的洞察力.
科学领域:
- 生物医学数据分析
- 计算生物学是一种计算生物学.
- 机器学习在医疗保健中的应用
背景情况:
- 高维,多尺度的生物医学数据在分类临床表型和预测结果方面存在挑战.
- 分析各种数据模式 (基因组学,成像,临床,实验室) 对于全面了解COVID-19等疾病至关重要.
结论:
- 在无监督和监督任务中,Sparse CCA和合作学习对分析高维,多式联络数据非常有效.
- 使用Word2Vec编码开发的四重模型实现了显著的预测性能 (AUC 0.87,准确率为0.77).
- 这种多模式方法为研究复杂的生物系统和改善患者结果预测提供了强大的工具.
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