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首页> 外文期刊>BMC Medical Informatics and Decision Making >Use of natural language processing to improve predictive models for imaging utilization in children presenting to the emergency department
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Use of natural language processing to improve predictive models for imaging utilization in children presenting to the emergency department

机译：使用自然语言处理来改进急救部门的儿童成像利用的预测模型

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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. 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. 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. 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.
机译：在患者ED访问期间检查医学成像利用率和与患者社会经济，人口统计学，人口统计学和临床​​因素相关的关联;利用这些相关因素开发预测模型，包括自然语言元素，以预测儿科ED的医学成像利用。儿科患者的数据来自2012-2016美国国家医院医疗医疗保健调查，以建立模型，以预测呈现给ED的儿童的成像。多变量逻辑回归模型采用结构化变量，如温度，心率，年龄和非结构化变量，如访问原因，免费文本护理说明和分类中可用的组合数据。 NLP技术用于从非结构化数据中提取信息。在该研究中包含的27,665名儿科ED访问中，8394（30.3％）在ED中获得医学成像，包括具有计算断层扫描（CT）扫描的X射线和1367（4.9％）的6922（25.0％） 。在包括仅结构化变量的预测模型中，C统计值为0.71（95％CI：0.71），用于X射线的0.69（95％CI：0.68-0.70），0.77（95％CI） ：0.76-0.78）用于CT。包括仅限非结构化信息的模型具有0.81（95％CI：0.81-0.82）的C统计，用于X射线的0.82（95％CI：0.82-0.83），0.85（95％CI：0.83-0.86 ）对于CT扫描。当包括结构化变量和自由文本变量都有0.82（95％CI：0.82-0.83），用于X射线的0.83（95％CI：0.83-0.84），0.87（95 CT％CI：0.86-0.88）。 CT和X射线通常用于小儿ED中，其中三分之一的访问接收至少一个。患者的社会经济，人口统计学和临床​​因素呈现在ED分流时期与医学成像利用有关。分类中可用的结构化和非结构化变量组合的预测模型比使用结构化或非结构化变量的模型更好地表达了使用NLP在确定资源利用时使用的可能性。

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