Bressler Colleen J, Malthaner Lauren, Pondel Nicholas, Letson Megan M, Kline David, Leonard Julie C
Division of Child and Family Advocacy, Nationwide Children's Hospital, Columbus, OH, USA.
Nationwide Children's Hospital Section of Emergency Medicine, Columbus, OH, USA.
Child Maltreat. 2024 Feb;29(1):37-46. doi: 10.1177/10775595221127925. Epub 2022 Oct 7.
The objective of this study was to use natural language processing to query Emergency Medical Services (EMS) electronic health records (EHRs) to identify variables associated with child maltreatment. We hypothesized the variables identified would show an association between the Emergency Medical Services encounter and risk of a children maltreatment report. This study is a retrospective cohort study of children with an EMS encounter from 1/1/11-12/31/18. NLP of EMS EHRs was conducted to generate single words, bigrams and trigrams. Clinically plausible risk factors for child maltreatment were established, where presence of the word(s) indicated presence of the hypothesized risk factor. The EMS encounters were probabilistically linked to child maltreatment reports. Univariable associations were assessed, and a multivariable logistic regression was conducted to determine a final set of predictors. 11 variables showed an association in the multivariable modeling. Sexual, abuse, chronic condition, developmental delay, unconscious on arrival, criminal activity/police, ingestion/inhalation/exposure, and <2 years old showed positive associations with child maltreatment reports. Refusal and DOA/PEA/asystole held negative associations. This study demonstrated that through EMS EHRs, risk factors for child maltreatment can be identified. A future direction of this work include developing a tool that screens EMS EHRs for households at risk for maltreatment.
本研究的目的是使用自然语言处理技术查询紧急医疗服务（EMS）电子健康记录（EHR），以识别与儿童虐待相关的变量。我们假设所识别出的变量将显示出紧急医疗服务遭遇与儿童虐待报告风险之间的关联。本研究是一项对2011年1月1日至2018年12月31日期间遭遇紧急医疗服务的儿童进行的回顾性队列研究。对紧急医疗服务电子健康记录进行自然语言处理，以生成单个单词、双词和三词。确定了临床上合理的儿童虐待风险因素，其中单词的出现表明假设的风险因素存在。紧急医疗服务遭遇与儿童虐待报告存在概率关联。评估了单变量关联，并进行了多变量逻辑回归以确定最终的预测因素集。11个变量在多变量模型中显示出关联。性、虐待、慢性病、发育迟缓、到达时无意识、犯罪活动/警方、摄入/吸入/接触以及<2岁与儿童虐待报告呈正相关。拒绝以及到达现场死亡/无脉电活动/心搏停止呈负相关。本研究表明，通过紧急医疗服务电子健康记录，可以识别出儿童虐待的风险因素。这项工作未来的一个方向包括开发一种工具，用于筛查有虐待风险家庭的紧急医疗服务电子健康记录。
