PDF(Pubmed)
Abstract：
BACKGROUND: The early identification of outbreaks of both known and novel influenza-like illnesses (ILIs) is an important public health problem.
OBJECTIVE: This study aimed to describe the design and testing of a tool that detects and tracks outbreaks of both known and novel ILIs, such as the SARS-CoV-2 worldwide pandemic, accurately and early.
METHODS: This paper describes the ILI Tracker algorithm that first models the daily occurrence of a set of known ILIs in hospital emergency departments in a monitored region using findings extracted from patient care reports using natural language processing. We then show how the algorithm can be extended to detect and track the presence of an unmodeled disease that may represent a novel disease outbreak.
RESULTS: We include results based on modeling diseases like influenza, respiratory syncytial virus, human metapneumovirus, and parainfluenza for 5 emergency departments in Allegheny County, Pennsylvania, from June 1, 2014, to May 31, 2015. We also include the results of detecting the outbreak of an unmodeled disease, which in retrospect was very likely an outbreak of the enterovirus D68 (EV-D68).
CONCLUSIONS: The results reported in this paper provide support that ILI Tracker was able to track well the incidence of 4 modeled influenza-like diseases over a 1-year period, relative to laboratory-confirmed cases, and it was computationally efficient in doing so. The system was also able to detect a likely novel outbreak of EV-D68 early in an outbreak that occurred in Allegheny County in 2014 as well as clinically characterize that outbreak disease accurately.
OBJECTIVE: This study aimed to describe the design and testing of a tool that detects and tracks outbreaks of both known and novel ILIs, such as the SARS-CoV-2 worldwide pandemic, accurately and early.
METHODS: This paper describes the ILI Tracker algorithm that first models the daily occurrence of a set of known ILIs in hospital emergency departments in a monitored region using findings extracted from patient care reports using natural language processing. We then show how the algorithm can be extended to detect and track the presence of an unmodeled disease that may represent a novel disease outbreak.
RESULTS: We include results based on modeling diseases like influenza, respiratory syncytial virus, human metapneumovirus, and parainfluenza for 5 emergency departments in Allegheny County, Pennsylvania, from June 1, 2014, to May 31, 2015. We also include the results of detecting the outbreak of an unmodeled disease, which in retrospect was very likely an outbreak of the enterovirus D68 (EV-D68).
CONCLUSIONS: The results reported in this paper provide support that ILI Tracker was able to track well the incidence of 4 modeled influenza-like diseases over a 1-year period, relative to laboratory-confirmed cases, and it was computationally efficient in doing so. The system was also able to detect a likely novel outbreak of EV-D68 early in an outbreak that occurred in Allegheny County in 2014 as well as clinically characterize that outbreak disease accurately.
摘要：
背景：早期识别已知和新型流感样疾病的爆发是一个重要的公共卫生问题。
目的：设计和测试一种检测和追踪已知和新型流感样疾病爆发的工具，例如SARS-CoV-19全球大流行，准确和早期。
方法：本文介绍了ILITracker算法，该算法首先使用自然语言处理从患者护理报告中提取的结果，对监测区域内医院急诊科的一组已知流感样疾病的每日发生率进行建模。然后，我们展示了如何扩展算法以检测和跟踪可能代表新疾病爆发的未建模疾病的存在。
结果：我们包括基于流感疾病建模的结果，呼吸道合胞病毒，人类偏肺病毒，2014年6月1日至2015年5月31日，宾夕法尼亚州阿勒格尼县五个急诊科的副流感。我们还包括检测未建模疾病爆发的结果，回想起来，很可能是肠道病毒EV-D68的爆发。
结论：本文报告的结果支持ILITracker能够在一年内很好地跟踪四种模拟流感样疾病的发病率，相对于实验室确诊病例，这样做在计算上是有效的。该系统也可以在2014年阿勒格尼县爆发的早期发现肠道病毒D68的新爆发，并在临床上准确表征该爆发疾病。
背景：
目的：设计和测试一种检测和追踪已知和新型流感样疾病爆发的工具，例如SARS-CoV-19全球大流行，准确和早期。
方法：本文介绍了ILITracker算法，该算法首先使用自然语言处理从患者护理报告中提取的结果，对监测区域内医院急诊科的一组已知流感样疾病的每日发生率进行建模。然后，我们展示了如何扩展算法以检测和跟踪可能代表新疾病爆发的未建模疾病的存在。
结果：我们包括基于流感疾病建模的结果，呼吸道合胞病毒，人类偏肺病毒，2014年6月1日至2015年5月31日，宾夕法尼亚州阿勒格尼县五个急诊科的副流感。我们还包括检测未建模疾病爆发的结果，回想起来，很可能是肠道病毒EV-D68的爆发。
结论：本文报告的结果支持ILITracker能够在一年内很好地跟踪四种模拟流感样疾病的发病率，相对于实验室确诊病例，这样做在计算上是有效的。该系统也可以在2014年阿勒格尼县爆发的早期发现肠道病毒D68的新爆发，并在临床上准确表征该爆发疾病。
背景：
