预测COVID-19的严重程度:机器学习方法的复制性和部署的挑战
Luwei Liu1, Wenyu Song2, Namrata Patil3
1Department of Medicine, Brigham & Women's Hospital, Boston, MA, USA.
International journal of medical informatics
|September 28, 2023
概括
电子健康记录 (EHR) 在医学中推进了人工智能. 然而,COVID-19严重程度模型中的不一致定义阻碍了临床应用,需要标准化的方法来进行可靠的预测.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 临床研究信息学 临床研究信息学
背景情况:
- 电子健康记录 (EHR) 越来越多地用于数据驱动的医疗应用和医疗保健中的人工智能 (AI).
- 电子健康记录提供了对开发医学AI模型至关重要的纵向患者数据.
- 变量和结果定义的标准化对于人工智能方法的临床适用性至关重要.
研究的目的:
- 通过使用EHR数据审查2019年新冠肺炎疾病 (COVID-19) 严重性的机器学习 (ML) 预测模型.
- 探索一个框架来标准化基于EHR信息的未来疾病严重程度模型的开发.
- 识别COVID-19严重性表型定义中的不一致性及其对模型概括性的影响.
主要方法:
- 对2020年1月1日至2022年2月15日期间发表的2967项研究进行了系统审查.
- 选择了135项独立研究,这些研究开发了ML模型,使用EHR数据预测COVID-19严重程度的结果.
- 分析了来自27个国家的135项研究,重点关注严重程度预测结果.
主要成果:
- 在审查的ML模型中,在COVID-19严重性表型的定义中观察到实质性的不一致性.
- 这些模型预测的结果与临床上公认的概念之间存在着显著的差距.
- 审查的研究显示了广泛的严重性预测结果,但缺乏定义统一性.
结论:
- 建议采用标准化,强大的临床输入指标和明确的结果定义,以减少偏差并提高模型通用性.
- 解决表型定义中的不一致性对于开发可靠和普遍适用的COVID-19严重性预测模型至关重要.
- 拟议的标准化框架有可能扩展到其他临床应用,超出COVID-19严重性预测范围.
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