重新设计机器学习表型以适应不断变化的COVID-19环境:N3C和RECOVER联盟的机器学习建模研究
Miles Crosskey1, Tomas McIntee2, Sandy Preiss3
1CoVar Applied Technologies, Durham, NC, USA.
The Lancet. Digital health
|August 26, 2025
概括
一个更新的机器学习模型准确地预测了长期COVID的可能性,即使在家庭测试和再感染. 这种方法改善了SARS-CoV-2感染研究的急性后果患者的鉴定.
科学领域:
- 机器学习在医疗保健中的应用
- 传染病流行病学
- 计算生物学
背景情况:
- 国家COVID队列协作 (N3C) 和NIHRECOVER倡议试图识别长期COVID患者.
- 最初的模型需要更新,因为在2022年后,家庭检测增加,数据缺失和再感染.
- 随着COVID-19的不断变化,需要重新设计机器学习管道.
研究的目的:
- 开发和完善机器学习管道,以识别长期COVID的患者.
- 调整模型以应对COVID-19检测和文档方面的变化.
- 在充满活力的流行环境中提供评估长期COVID流行的强有力的方法.
主要方法:
- 在N3C的72,745名患者记录上训练了更新的XGBoost模型.
- 在重叠的100天期间分析数据以计算长期COVID的概率.
- 该模型从2020年1月到2023年6月处理了5,875,065名患者记录,包括各种COVID-19指标和对急性感染/再感染的审查.
主要成果:
- 更新的模型在接收器操作特征曲线下的面积为0.90.
- 模型的精度和回忆可根据特定的用例要求进行调整.
- 在N3C库中发现的COVID-19阳性队列中,长期COVID的流行率约为10.4%.
结论:
- 修订后的模型有效地识别了长期COVID的可能性,而不依赖于固定的COVID-19指数日期.
- 这种方法适用于在家检测,怀疑或未检测的COVID-19患者以及再次感染的患者.
- 该方法作为维持和更新临床研究和操作中的机器学习管道的模型.
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