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Early Detection of Rotavirus Gastrointestinal Illness Outbreaks by MultipleData Sources and Detection Algorithms at a Pediatric Health System

机译:轮状病毒胃肠道疾病暴发的早期发现儿科医疗系统的数据源和检测算法

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摘要

Using data from over 450,000 pediatric encounters three data sources were evaluated for their ability to support early detection of a yearly outbreak of rotavirus disease: 1) Laboratory studies ordered, 2) Diagnosis codes, and 3) Free text “reason for visit” strings categorized as Gastrointestinal syndrome by a support vector machine software classifier. We found that in this setting the categorized free text analyzed through simple control charts detected each outbreak within 10 days of their beginning as determined by laboratory detection of rotavirus antigen (the gold standard). Outbreak detection by laboratory orders was delayed an average of 14 days and by diagnosis codes by an average of 20 days. We conclude that categorized text may provide a valuable basis for real-time detection of disease outbreaks.
机译:利用来自超过450,000例小儿科疾病的数据,对三个数据源的能力进行了评估,以支持其早期发现轮状病毒疾病的每年爆发:1)订购的实验室研究,2)诊断代码和3)自由文本“造访原因”字符串分类通过支持向量机软件分类器可诊断为胃肠道综合症。我们发现,在这种情况下,通过简单的控制图分析的分类自由文本在轮状病毒抗原(金标准)的实验室检测确定的爆发开始后的10天内检测到了每次爆发。实验室命令的暴发检测平均延迟了14天,诊断代码平均延迟了20天。我们得出结论,分类文本可能为实时检测疾病暴发提供有价值的基础。

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