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使用自动关键词识别软件从急诊科数据库监测苛性损伤。

Monitoring caustic injuries from emergency department databases using automatic keyword recognition software.

作者信息

Vignally P, Fondi G, Taggi F, Pitidis A

机构信息

National Institute of Health, Rome, Italy.

出版信息

Ann Burns Fire Disasters. 2011 Mar 31;24(1):14-6.

Abstract

In Italy the European Union Injury Database reports the involvement of chemical products in 0.9% of home and leisure accidents. The Emergency Department registry on domestic accidents in Italy and the Poison Control Centres record that 90% of cases of exposure to toxic substances occur in the home. It is not rare for the effects of chemical agents to be observed in hospitals, with a high potential risk of damage - the rate of this cause of hospital admission is double the domestic injury average. The aim of this study was to monitor the effects of injuries caused by caustic agents in Italy using automatic free-text recognition in Emergency Department medical databases. We created a Stata software program to automatically identify caustic or corrosive injury cases using an agent-specific list of keywords. We focused attention on the procedure's sensitivity and specificity. Ten hospitals in six regions of Italy participated in the study. The program identified 112 cases of injury by caustic or corrosive agents. Checking the cases by quality controls (based on manual reading of ED reports), we assessed 99 cases as true positive, i.e. 88.4% of the patients were automatically recognized by the software as being affected by caustic substances (99% CI: 80.6%- 96.2%), that is to say 0.59% (99% CI: 0.45%-0.76%) of the whole sample of home injuries, a value almost three times as high as that expected (p < 0.0001) from European codified information. False positives were 11.6% of the recognized cases (99% CI: 5.1%- 21.5%). Our automatic procedure for caustic agent identification proved to have excellent product recognition capacity with an acceptable level of excess sensitivity. Contrary to our a priori hypothesis, the automatic recognition system provided a level of identification of agents possessing caustic effects that was significantly much greater than was predictable on the basis of the values from current codifications reported in the European Database.

摘要

在意大利,欧盟伤害数据库报告称,化学产品涉及0.9%的家庭和休闲事故。意大利国内事故急诊部登记处和毒物控制中心记录显示,90%的有毒物质接触病例发生在家中。在医院观察到化学制剂的影响并不罕见,其造成损害的潜在风险很高——因这种原因入院的比例是家庭伤害平均水平的两倍。本研究的目的是利用急诊部医疗数据库中的自动自由文本识别功能,监测意大利苛性剂造成的伤害影响。我们创建了一个Stata软件程序,使用特定于某种制剂的关键词列表自动识别苛性或腐蚀性伤害病例。我们重点关注该程序的敏感性和特异性。意大利六个地区的十家医院参与了这项研究。该程序识别出112例由苛性或腐蚀性制剂造成的伤害病例。通过质量控制(基于对急诊报告的人工阅读)检查这些病例,我们评估出99例为真阳性,即88.4%的患者被该软件自动识别为受到苛性物质影响(99%置信区间:80.6%-96.2%),也就是说占家庭伤害整个样本的0.59%(99%置信区间:0.45%-0.76%),这一数值几乎是欧洲编纂信息预期值的三倍(p<0.0001)。假阳性占已识别病例的11.6%(99%置信区间:5.1%-21.5%)。我们用于识别苛性制剂的自动程序被证明具有出色的产品识别能力,且具有可接受的过度敏感性水平。与我们的先验假设相反,自动识别系统对具有苛性作用的制剂的识别水平显著高于根据欧洲数据库中当前编纂值预测的水平。

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