Fever detection from free-text clinical records for biosurveillance.
Fever detection from free-text clinical records for biosurveillance.
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DOI：
10.1016/j.jbi.2004.03.002
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发表时间：
2004-04
影响因子：
4.5
通讯作者：
Wagner MM
中科院分区：
文献类型：
--
作者：
Chapman WW;Dowling JN;Wagner MM
发热性疾病病例的自动检测可能具有通过识别发热性疾病的异常数量或与诊断特定综合征（如发热性呼吸综合征）的其他信息相结合来早期检测传染病暴发的潜力。在大多数机构，发热信息仅包含在自由文本临床记录中。我们比较了三种发烧检测算法从自由文本中检测发烧的灵敏度和特异性。关键词CC和CoCo根据分诊主诉对患者进行分类;关键词HP根据口述的急诊科报告对患者进行分类。关键词HP最敏感（敏感性0.98，特异性0.89），关键词CC最特异（敏感性0.61，特异性1.0）。由于主诉比急诊科报告更早可用，因此我们建议使用一种组合应用程序，该应用程序根据主诉对患者进行分类，然后根据急诊科报告进行分类，一旦报告可用。
Automatic detection of cases of febrile illness may have potential for early detection of outbreaks of infectious disease either by identification of anomalous numbers of febrile illness or in concert with other information in diagnosing specific syndromes, such as febrile respiratory syndrome. At most institutions, febrile information is contained only in free-text clinical records. We compared the sensitivity and specificity of three fever detection algorithms for detecting fever from free-text. Keyword CC and CoCo classified patients based on triage chief complaints; Keyword HP classified patients based on dictated emergency department reports. Keyword HP was the most sensitive (sensitivity 0.98, specificity 0.89), and Keyword CC was the most specific (sensitivity 0.61, specificity 1.0). Because chief complaints are available sooner than emergency department reports, we suggest a combined application that classifies patients based on their chief complaint followed by classification based on their emergency department report, once the report becomes available.
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影响因子：
6.2
作者：
Chen, SY;Su, CP;Chen, WJ
通讯作者：
Chen, WJ
DOI：
10.1197/jamia.m1330
发表时间：
2003-09-01
影响因子：
6.4
作者：
Chapman, WW;Cooper, GF;Wagner, MM
通讯作者：
Wagner, MM
DOI：
10.1016/s1386-5056(02)00053-9
发表时间：
2002-12-04
影响因子：
4.9
作者：
Hahn, U;Romacker, M;Schulz, S
通讯作者：
Schulz, S
影响因子：
3.5
作者：
Reis, Ben Y;Mandl, Kenneth D
通讯作者：
Mandl, Kenneth D
影响因子：
6.2
作者：
Irvin, CB;Nouhan, PP;Rice, K
通讯作者：
Rice, K