论文标题
在人口规模上使用大流行病记录改善基于ECG的COVID-19诊断和死亡率预测
Improving ECG-based COVID-19 diagnosis and mortality predictions using pre-pandemic medical records at population-scale
论文作者
论文摘要
诸如Covid-19之类的大流行爆发出乎意料的发生，并且由于对全球健康的潜在毁灭性后果而需要立即采取行动。可以使用心电图（ECG）等护理的常规评估来开发用于识别有风险个人的预测模型。但是，通常很少有临床上注销的医学数据，尤其是在大流行的早期阶段，无法开发准确的预测模型。在这种情况下，可以利用历史前大体病健康记录来估计初步模型，然后可以根据有限的可用大流行数据进行微调。这项研究表明，通过证明与三种不同的Covid-19相关诊断和预后预测任务相比，这种方法可以有效地改善培训前的训练前深学习模型，可以有效地工作。类似的转移学习策略对于在未来的大流行暴发中开发及时的人工智能解决方案可能很有用。
Pandemic outbreaks such as COVID-19 occur unexpectedly, and need immediate action due to their potential devastating consequences on global health. Point-of-care routine assessments such as electrocardiogram (ECG), can be used to develop prediction models for identifying individuals at risk. However, there is often too little clinically-annotated medical data, especially in early phases of a pandemic, to develop accurate prediction models. In such situations, historical pre-pandemic health records can be utilized to estimate a preliminary model, which can then be fine-tuned based on limited available pandemic data. This study shows this approach -- pre-train deep learning models with pre-pandemic data -- can work effectively, by demonstrating substantial performance improvement over three different COVID-19 related diagnostic and prognostic prediction tasks. Similar transfer learning strategies can be useful for developing timely artificial intelligence solutions in future pandemic outbreaks.
