Using natural language processing to identify child maltreatment in health systems.
Using natural language processing to identify child maltreatment in health systems.
复制标题
使用自然语言处理来识别卫生系统中的儿童虐待行为。
DOI：
10.1016/j.chiabu.2023.106090
复制
发表时间：
2023
影响因子：
4.8
通讯作者：
Penfold,RobertB
中科院分区：
文献类型：
--
作者：
Negriff,Sonya;Lynch,FrancesL;Cronkite,DavidJ;Pardee,RoyE;Penfold,RobertB
背景从电子健康记录中获得的儿童虐待 (CM) 率远低于国家儿童福利患病率显示的水平。有必要了解如何记录 CM，以改进报告和监测。目的检查在门诊病历记录中使用自然语言处理 (NLP) 是否可以识别 ICD 诊断代码中未记录的 CM 病例、ICD 和 NLP 的儿童虐待编码之间的重叠，以及年龄、性别或种族/民族的任何差异。方法华盛顿凯撒医疗机构 (KPWA) 内 0-18 岁儿童的门诊病历记录2018-2020 年用于检查一组选定的虐待相关术语，这些术语被分类为概念唯一标识符 (CUI)。对每个 CUI 的文本片段进行手动审核，以标记经过验证的案例并重新训练 NLP 算法。结果 NLP 结果表明，参考 CM 的注释的粗略率为 1.55% 至 2.36%（2018-2020 年）。 ICD代码识别出的CM率为每1000名儿童3.32例，而NLP识别出的CM率为每1000名儿童37.38例。从 ICD 到 NLP，虐待识别率增加最多的群体是青少年（13-18 岁）、女性、美洲原住民儿童和享受医疗补助的群体。值得注意的是，在使用 NLP 时，所有亚组的虐待率均显着升高。结论 NLP 的使用大大增加了受 CM 影响的儿童的估计数量。准确地捕捉这一人群将有助于识别存在心理健康症状高风险的弱势青少年。
BackgroundRates of child maltreatment (CM) obtained from electronic health records are much lower than national child welfare prevalence rates indicate. There is a need to understand how CM is documented to improve reporting and surveillance.ObjectivesTo examine whether using natural language processing (NLP) in outpatient chart notes can identify cases of CM not documented by ICD diagnosis code, the overlap between the coding of child maltreatment by ICD and NLP, and any differences by age, gender, or race/ethnicity.MethodsOutpatient chart notes of children age 0–18 years old within Kaiser Permanente Washington (KPWA) 2018–2020 were used to examine a selected set of maltreatment-related terms categorized into concept unique identifiers (CUI). Manual review of text snippets for each CUI was completed to flag for validated cases and retrain the NLP algorithm.ResultsThe NLP results indicated a crude rate of 1.55 % to 2.36 % (2018–2020) of notes with reference to CM. The rate of CM identified by ICD code was 3.32 per 1000 children, whereas the rate identified by NLP was 37.38 per 1000 children. The groups that increased the most in identification of maltreatment from ICD to NLP were adolescents (13–18 yrs. old), females, Native American children, and those on Medicaid. Of note, all subgroups had substantially higher rates of maltreatment when using NLP.ConclusionsUse of NLP substantially increased the estimated number of children who have been impacted by CM. Accurately capturing this population will improve identification of vulnerable youth at high risk for mental health symptoms.