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首页> 外文期刊>BMC Medical Informatics and Decision Making >Screening pregnant women for suicidal behavior in electronic medical records: diagnostic codes vs. clinical notes processed by natural language processing
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Screening pregnant women for suicidal behavior in electronic medical records: diagnostic codes vs. clinical notes processed by natural language processing

机译：筛查孕妇电子病历中的自杀行为：自然语言处理的诊断代码与临床注释

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We examined the comparative performance of structured, diagnostic codes vs. natural language processing (NLP) of unstructured text for screening suicidal behavior among pregnant women in electronic medical records (EMRs). Women aged 10–64 years with at least one diagnostic code related to pregnancy or delivery (N = 275,843) from Partners HealthCare were included as our “datamart.” Diagnostic codes related to suicidal behavior were applied to the datamart to screen women for suicidal behavior. Among women without any diagnostic codes related to suicidal behavior (n = 273,410), 5880 women were randomly sampled, of whom 1120 had at least one mention of terms related to suicidal behavior in clinical notes. NLP was then used to process clinical notes for the 1120 women. Chart reviews were performed for subsamples of women. Using diagnostic codes, 196 pregnant women were screened positive for suicidal behavior, among whom 149 (76%) had confirmed suicidal behavior by chart review. Using NLP among those without diagnostic codes, 486 pregnant women were screened positive for suicidal behavior, among whom 146 (30%) had confirmed suicidal behavior by chart review. The use of NLP substantially improves the sensitivity of screening suicidal behavior in EMRs. However, the prevalence of confirmed suicidal behavior was lower among women who did not have diagnostic codes for suicidal behavior but screened positive by NLP. NLP should be used together with diagnostic codes for future EMR-based phenotyping studies for suicidal behavior.
机译：我们检查了结构化，诊断代码与非结构化文本的自然语言处理（NLP）在筛查电子病历（EMR）中孕妇自杀行为方面的比较性能。年龄至少在10-64岁之间且具有Partners HealthCare至少一项与怀孕或分娩相关的诊断代码（N = 275,843）的女性被列为我们的“数据集市”。与自杀行为相关的诊断代码已应用于数据集市，以筛查妇女的自杀行为。在没有任何与自杀行为相关的诊断代码的女性中（n = 273,410），随机抽取了5880名女性，其中1120名女性在临床笔记中至少提到了与自杀行为有关的术语。然后，使用NLP处理1120名女性的临床笔记。对女性子样本进行了图表审查。使用诊断代码，对196名孕妇的自杀行为进行了筛查，其中有149名（76％）通过图表检查确认了自杀行为。在没有诊断代码的人中使用NLP，对486名孕妇的自杀行为进行了筛查，其中有146名（30％）已通过图表审查确认了自杀行为。 NLP的使用大大提高了EMR中筛查自杀行为的敏感性。但是，在没有自杀行为诊断代码但通过NLP筛查为阳性的女性中，确认的自杀行为患病率较低。 NLP应该与诊断代码一起用于将来基于EMR的自杀行为表型研究。

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