一个全面的基准用于COVID-19预测建模使用密集护理的电子健康记录
Junyi Gao1,2, Yinghao Zhu3, Wenqing Wang3
1Centre for Medical Informatics, University of Edinburgh, EH16 4UX Edinburgh, UK.
Patterns (New York, N.Y.)
|April 22, 2024
概括
这项研究引入了COVID-19患者结果的新预测任务,开发了电子健康记录数据的评估指标和模型. 这些发现旨在改善用于流行病预测建模的深度学习和机器学习.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 流行病学 流行病学
背景情况:
- COVID-19大流行强调了医疗保健中需要预测性深度学习模型的需要.
- 在设计实际的预测任务,公平地比较模型,并选择它们用于临床使用方面存在挑战.
研究的目的:
- 为重症监护中的COVID-19患者引入和评估新的预测任务:结果特定的停留时间和早期死亡率预测.
- 解决临床预测任务设计,模型比较和选择方面的挑战.
主要方法:
- 开发了两个新的预测任务,反映了COVID-19重症监护病人的临床现实.
- 创建了评估指标,模型适应设计和开源数据预处理管道.
- 评估了18个预测模型 (临床评分,传统的ML,基本和高级深度学习) 使用两个现实世界的COVID-19电子健康记录数据集.
主要成果:
- 从两个现实世界的COVID-19电子健康记录数据集获得了对比结果.
- 所有的结果和受过训练的模型都在在线平台上发布,供临床和研究使用.
- 该研究为评估重症监护机构的预测模型提供了一个框架.
结论:
- 开发的预测任务,指标和模型推进了大流行预测建模的深度学习和机器学习研究.
- 这项工作促进了预测模型在临床环境中的实际应用,以改善患者护理.
- 数据,模型和管道的开源发布促进了可复制性和进一步的健康信息学研究.
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