基因和调查数据改善了长期COVID的机器学习模型的性能
Wei-Qi Wei1, Christopher Guardo1, Srushti Gandireddy1
1Vanderbilt University Medical Center.
Research square
|January 10, 2024
概括
研究人员通过将健康调查,移动设备和遗传数据与现有的电子健康记录模型相结合,改进了长期COVID预测. 这种增强的方法提高了在识别持久后COVID症状的患者的准确性.
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
- 医疗信息学医学信息学
背景情况:
- 在全球范围内,超过2亿人经历了SARS-CoV-2感染后的持续症状 (长期COVID).
- 国家COVID队列协作 (N3C) 开发了一种使用电子健康记录 (EHR) 识别长期COVID患者的机器学习模型.
- 多模式数据集成以改善长期COVID预测的潜力在很大程度上仍未被探索.
研究的目的:
- 为了提高长期COVID识别的预测准确度.
- 评估将健康调查,移动设备和遗传数据纳入现有基于EHR的模型的实用性.
- 在各种数据模式中识别关键预测因素.
主要方法:
- 利用了来自我们所有人计划的17,755名SARS-CoV-2感染个人的队列.
- 应用并扩展了N3C长期COVID预测模型.
- 经过测试的机器学习基础设施,包括极端梯度增强和卷积神经网络,分别用于调查/移动和遗传数据.
- 使用像接收器操作特征 (ROC) 曲线下的面积 (AUC) 等指标评估模型性能.
主要成果:
- 极端梯度增强和卷积神经网络分别显示了调查/移动和遗传数据的高性能.
- 结合多模式数据 (调查,遗传,移动) 与原来的N3C EHR-only模型相比,显著提高了模型特异性和AUC.
- 在整合数据集中确定了对长期COVID预测的关键因素.
结论:
- 整合各种数据来源,包括健康调查,移动数据和遗传学,大大改善了长期COVID的预测.
- 机器学习模型可以有效地利用多模式数据来更准确地识别患有持续后COVID疾病的患者.
- 这种增强的预测能力可以促进针对性干预和长期COVID的研究.
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