一个基于变压器的框架,用于对抗高血压治疗对COVID-19感染风险的影响的反事实估计 - 一个概念验证研究
Tran Qb Tran1, Stefanie Lip1,2, Honghan Wu3
1School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, United Kingdom.
变压器模型估计了抗高血压剂的COVID-19风险. β-阻断剂和通道阻断剂显示出保护作用,而ACE抑制剂并没有显著增加风险.
科学领域:
- * 医疗保健中的人工智能
- * 因果推理和机器学习
- * 药物监督和药物安全
背景情况:
- * 变压器神经网络擅长分析复杂,高维的时间序列数据.
- *本研究探讨了它们在现实世界医疗保健数据中的应用,以估计治疗效果.
- *抗高血压药物暴露及其与COVID-19风险的关联是主要的例子.
研究的目的:
- * 应用变压器-X学习者框架用于使用真实世界患者数据进行因果推断.
- * 估计特定抗高血压药物对COVID-19感染风险的影响.
- *与传统方法相比,评估这种先进框架的性能.
主要方法:
- * 一项病例控制研究包括303,220名40岁以上的NHS大格拉斯哥和克莱德的患者在最初的COVID-19浪潮期间.
- *采用了变压器-X学习者框架,整合了药物使用模式和并发症,以控制混.
- *估计了ACEI,BB,CCB,THZ和他类药物对180天SARS-CoV-2感染风险的个体和平均治疗效应.
主要成果:
- * 变压器-X-学习器框架表现出卓越的性能,F1得分为0.82,AUPRC得分为0.78.
- * ангиотензин转化酶抑制剂 (ACEI) 对COVID-19风险的影响微不足道 (0.97%±5.5%).
- *β阻塞剂 (BBs) (-8.3%±7.3%) 和通道阻塞剂 (CCBs) (-9.7%±8.1%) 显示出保护作用,而他类药物 (3.5%±6.1%) 和 thiazides (THZs) (4.3%±10.8%) 显示出轻微的风险增加.
结论:
- * ACEI似乎没有显著提高COVID-19感染风险.
- * 观察到的BB和CCB的保护作用需要进一步调查.
- *基于变压器的因果推断模型显示出在复杂的健康场景中评估治疗安全性和有效性的前景.
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