论文标题
全面地通过人类的机器学习来全面识别长期的卷vid文章
Comprehensively identifying Long Covid articles with human-in-the-loop machine learning
论文作者
论文摘要
Covid-19幸存者中有很大一部分经历了经常影响日常生活的持续多系统症状，这种疾病被称为SARS-COV-2感染的长期或急性后的次要症状。但是，由于没有标准化或共识术语，因此确定与长期相关的科学文章是具有挑战性的。我们开发了一个迭代的人类在循环机器学习框架中，将数据编程与主动学习结合到强大的集合模型中，比其他方法表现出更高的特异性和更高的灵敏度。对长期共同集合的分析表明，（1）最长的共vid文章在命名该条件时并不是指任何名称（2）的长期covid，文献中最常使用的名称是长的，而（3）长covid与各种身体系统中的障碍有关。 Litcovid门户网站可在线搜索，可在线搜索：https：//www.ncbi.nlm.nlm.nih.gov/research/coronavirus/docsum?filters=e_condition.longcondition.longcovid.longcovid
A significant percentage of COVID-19 survivors experience ongoing multisystemic symptoms that often affect daily living, a condition known as Long Covid or post-acute-sequelae of SARS-CoV-2 infection. However, identifying scientific articles relevant to Long Covid is challenging since there is no standardized or consensus terminology. We developed an iterative human-in-the-loop machine learning framework combining data programming with active learning into a robust ensemble model, demonstrating higher specificity and considerably higher sensitivity than other methods. Analysis of the Long Covid collection shows that (1) most Long Covid articles do not refer to Long Covid by any name (2) when the condition is named, the name used most frequently in the literature is Long Covid, and (3) Long Covid is associated with disorders in a wide variety of body systems. The Long Covid collection is updated weekly and is searchable online at the LitCovid portal: https://www.ncbi.nlm.nih.gov/research/coronavirus/docsum?filters=e_condition.LongCovid
