OBJECTIVE To examine the association between the medical imaging utilization and information related to patients' socioeconomic, demographic and clinical factors during the patients' ED visits; and to develop predictive models using these associated factors including natural language elements to predict the medical imaging utilization at pediatric ED. METHODS Pediatric patients' data from the 2012-2016 United States National Hospital Ambulatory Medical Care Survey was included to build the models to predict the use of imaging in children presenting to the ED. Multivariable logistic regression models were built with structured variables such as temperature, heart rate, age, and unstructured variables such as reason for visit, free text nursing notes and combined data available at triage. NLP techniques were used to extract information from the unstructured data. RESULTS Of the 27,665 pediatric ED visits included in the study, 8394 (30.3%) received medical imaging in the ED, including 6922 (25.0%) who had an X-ray and 1367 (4.9%) who had a computed tomography (CT) scan. In the predictive model including only structured variables, the c-statistic was 0.71 (95% CI: 0.70-0.71) for any imaging use, 0.69 (95% CI: 0.68-0.70) for X-ray, and 0.77 (95% CI: 0.76-0.78) for CT. Models including only unstructured information had c-statistics of 0.81 (95% CI: 0.81-0.82) for any imaging use, 0.82 (95% CI: 0.82-0.83) for X-ray, and 0.85 (95% CI: 0.83-0.86) for CT scans. When both structured variables and free text variables were included, the c-statistics reached 0.82 (95% CI: 0.82-0.83) for any imaging use, 0.83 (95% CI: 0.83-0.84) for X-ray, and 0.87 (95% CI: 0.86-0.88) for CT. CONCLUSIONS Both CT and X-rays are commonly used in the pediatric ED with one third of the visits receiving at least one. Patients' socioeconomic, demographic and clinical factors presented at ED triage period were associated with the medical imaging utilization. Predictive models combining structured and unstructured variables available at triage performed better than models using structured or unstructured variables alone, suggesting the potential for use of NLP in determining resource utilization.

中文翻译：

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使用自然语言处理来改善预测模型，以便为急诊科的儿童提供影像学利用。

目的探讨在急诊就诊期间医学影像利用与与患者社会经济，人口统计学和临床​​因素有关的信息之间的关联；并使用这些相关因素（包括自然语言元素）开发预测模型，以预测小儿ED的医学影像利用率。方法纳入了2012-2016年美国国家医院门诊医疗调查中的儿科患者数据，以建立模型来预测在急诊室就诊的儿童中影像学的使用。使用结构变量（例如温度，心率，年龄）和非结构变量（例如探访原因，免费护理说明和分诊时可用的组合数据）构建多变量逻辑回归模型。NLP技术用于从非结构化数据中提取信息。结果该研究包括27665例儿科ED访视，其中8394例接受了ED的医学成像，包括6922例（25.0％）进行了X射线检查和1367例（4.9％）进行了计算机断层扫描（CT）。扫描。在仅包含结构变量的预测模型中，任何成像用途的c统计量均为0.71（95％CI：0.70-0.71），X射线的c统计量为0.69（95％CI：0.68-0.70），以及0.77（95％CI） ：0.76-0.78）（适用于CT）。仅包含非结构化信息的模型的任何成像用途的c统计量均为0.81（95％CI：0.81-0.82），X射线的c-统计量为0.82（95％CI：0.82-0.83），0.85（95％CI：0.83-0.86） ）进行CT扫描。当同时包含结构化变量和自由文本变量时，对于任何成像用途，c统计量均达到0.82（95％CI：0.82-0.83），对于X射线，其c统计量达到0.83（95％CI：0.83-0.84），CT为0.87（95％CI：0.86-0.88）。结论小儿急诊通常使用CT和X射线检查，三分之一的就诊者至少接受一次。急诊分诊期间出现的患者社会经济，人口统计学和临床​​因素与医学影像利用有关。将分类中可用的结构化和非结构化变量组合在一起的预测模型比仅使用结构化或非结构化变量的模型表现更好，这表明使用NLP来确定资源利用率的潜力。