论文标题
分析NIH N3C的历史诊断代码数据，并使用深度学习恢复程序，以确定长期相关的风险因素
Analyzing historical diagnosis code data from NIH N3C and RECOVER Programs using deep learning to determine risk factors for Long Covid
论文作者
论文摘要
SARS-COV-2感染（PASC）或Long Covid的急性后遗症是一种新兴的疾病，在几名CoVID-19的阳性诊断患者中已经观察到。历史电子健康记录（EHR）（例如诊断代码，实验室结果和临床笔记）已使用深度学习分析，并已用于预测未来的临床事件。在本文中，我们提出了一种可解释的深度学习方法，以分析来自国家Covid队列集体（N3C）的历史诊断代码数据，以找到有助于发展长期Covid的风险因素。使用我们的深度学习方法，我们能够预测患者在第一次为每个患者的诊断代码列表中长达45天的临时诊断代码列表，每位患者的诊断范围为45天，精度为70.48 \％。然后，我们能够使用梯度加权类激活映射（GRAGCAM）检查训练的模型，以使每个输入诊断得分。得分最高的诊断被认为是对患者进行正确预测最重要的诊断。我们还提出了一种总结我们队列中每个患者的最佳诊断的方法，并查看其时间趋势，以确定哪些代码有助于阳性长期的共同诊断。
Post-acute sequelae of SARS-CoV-2 infection (PASC) or Long COVID is an emerging medical condition that has been observed in several patients with a positive diagnosis for COVID-19. Historical Electronic Health Records (EHR) like diagnosis codes, lab results and clinical notes have been analyzed using deep learning and have been used to predict future clinical events. In this paper, we propose an interpretable deep learning approach to analyze historical diagnosis code data from the National COVID Cohort Collective (N3C) to find the risk factors contributing to developing Long COVID. Using our deep learning approach, we are able to predict if a patient is suffering from Long COVID from a temporally ordered list of diagnosis codes up to 45 days post the first COVID positive test or diagnosis for each patient, with an accuracy of 70.48\%. We are then able to examine the trained model using Gradient-weighted Class Activation Mapping (GradCAM) to give each input diagnoses a score. The highest scored diagnosis were deemed to be the most important for making the correct prediction for a patient. We also propose a way to summarize these top diagnoses for each patient in our cohort and look at their temporal trends to determine which codes contribute towards a positive Long COVID diagnosis.
