人工智能算法的一致性和概括与前omicron和omicron时代的现实世界临床数据
Gulsen Yilmaz1,2, Sevilay Sezer2, Aliye Bastug3
1Department of Medical Biochemistry, Ankara Yıldırım Beyazıt University, Ankara, Turkey.
Heliyon
|February 15, 2024
概括
这项研究验证了一种深度学习算法,使用实验室标记和年龄预测COVID-19的严重程度. 该算法在Omicron前和Omicron变体时期显示了一致的现实世界性能,有助于临床决策.
科学领域:
- 传染性疾病 传染性疾病
- 人工智能在医学中的应用
- 计算生物学 计算生物学
背景情况:
- 病毒进化,包括SARS-CoV-2,导致新的变种.
- 预测COVID-19疾病严重程度对于患者管理至关重要.
- 评估不同病毒时代的算法性能至关重要.
研究的目的:
- 评估深度学习 (DL) 算法的与现实世界的临床数据的一致性.
- 评估DL算法在预测COVID-19疾病严重程度方面的准确性.
- 为了比较在Omicron前和Omicron变体时期的算法性能.
主要方法:
- 使用DL算法结合实验室标记和年龄.
- 在Omicron前的数据上训练并验证了算法.
- 在美国,西班牙和土耳其的Omicron前和Omicron时代临床数据上测试了算法.
- 将算法预测与实际临床结果进行了比较.
主要成果:
- 所有数据集的一致性指数值在0.71到0.81.8之间.
- 在这两个时期,该算法在安卡拉市医院队列中的严重病例中显示了0.78或更高的负预测值 (NPV).
- 在前Omicron和Omicron时代之间的表现保持一致.
结论:
- DL算法在预测不同SARS-CoV-2变种时期的COVID-19严重程度方面表现可靠.
- 这些发现支持使用该算法用于现实世界的临床决策.
- 该研究强调了人工智能在管理不断发展的传染病方面的潜力.
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