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在大规模训练的基于变压器的模型声称数据用于预测严重的COVID-19疾病进展

Manuel Lentzen, Thomas Linden, Sai Veeranki

IEEE journal of biomedical and health informatics

|June 22, 2023

概括

机器学习模型可以使用电子健康记录 (EHR) 预测严重的COVID-19结果. 基于变压器的模型,增强了更多的数据,在预测急性呼吸道症状 (ARM) 风险方面显示出更高的准确性.

科学领域:

医疗信息学医疗信息学
医疗保健中的人工智能
计算生物学计算生物学

背景情况:

在COVID-19等大流行期间,医疗保健系统面临着巨大的压力.
电子健康记录 (EHR) 是丰富的数据来源,但由于不规则的时间,对机器学习 (ML) 具有挑战性.
基于变压器的模型对分析复杂的EHR数据显示出前景.

研究的目的:

开发和评估先进的ML模型,用于预测COVID-19患者的严重疾病进展.
通过使用全面的EHR数据,利用变压器架构来增强风险预测.
改善患者分层和精准医学方法,用于呼吸系统疾病.

主要方法:

扩展了Med-BERT模型,将年龄,性别,药物,定量措施和状态信息纳入.
预先训练了约988万份来自350万名患者的电子病历模型.
开发并将基于变压器的模型与随机森林,XGBoost和RETAIN进行比较,以预测80211名COVID-19患者的急性呼吸道表现 (ARM) 风险.
利用集成梯度和贝叶斯网络来实现模型的可解释性.

主要成果:

与传统的ML方法相比,基于变压器的模型在预测ARM风险方面表现出更高的准确性.
改进的Med-BERT模型有效地利用了各种电子健康记录功能来预测风险.
模型解释性方法确定了关键的预测特征.
该模型显示了不同医疗保健数据的适应性,包括奥地利住院患者数据.

结论:

基于变压器的预测模型为精准医学在管理传染病方面提供了一个有希望的途径.
先进的ML模型可以通过在危机期间识别高风险患者来显著帮助医疗保健系统.
该研究强调了利用大规模EHR数据与复杂的人工智能用于临床决策支持的潜力.

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