Emory University School of Medicine, Department of Biomedical Informatics, Atlanta, Georgia.
Emory University School of Medicine, Department of Emergency Medicine, Atlanta, Georgia.
West J Emerg Med. 2022 Sep 12;23(5):781-788. doi: 10.5811/westjem.2022.7.56726.
Intimate partner violence (IPV) is defined as sexual, physical, psychological, or economic violence that occurs between current or former intimate partners. Victims of IPV may seek care for violence-related injuries in healthcare settings, which makes recognition and intervention in these facilities critical. In this study our goal was to develop an algorithm using natural language processing (NLP) to identify cases of IPV within emergency department (ED) settings.
In this observational cohort study, we extracted unstructured physician and advanced practice provider, nursing, and social worker notes from hospital electronic health records (EHR). The recorded clinical notes and patient narratives were screened for a set of 23 situational terms, derived from the literature on IPV (ie, assault by spouse), along with an additional set of 49 extended situational terms, extracted from known IPV cases (ie, attack by spouse). We compared the effectiveness of the proposed model with detection of IPV-related International Classification of Diseases, 10th Revision, codes.
We included in the analysis a total of 1,064,735 patient encounters (405,303 patients who visited the ED of a Level I trauma center) from January 2012-August 2020. The outcome was identification of an IPV-related encounter. In this study we used information embedded in unstructured EHR data to develop a NLP algorithm that employs clinical notes to identify IPV visits to the ED. Using a set of 23 situational terms along with 49 extended situational terms, the algorithm successfully identified 7,399 IPV-related encounters representing 5,975 patients; the algorithm achieved 99.5% precision in detecting positive cases in our sample of 1,064,735 ED encounters.
Using a set of pre-defined IPV-related terms, we successfully developed a novel natural language processing algorithm capable of identifying intimate partner violence.
亲密伴侣暴力（IPV）是指当前或前任亲密伴侣之间发生的性、身体、心理或经济暴力。IPV 的受害者可能会因与暴力相关的伤害到医疗保健机构寻求治疗，这使得在这些机构中识别和干预至关重要。在这项研究中，我们的目标是使用自然语言处理（NLP）开发一种算法，以识别急诊科（ED）环境中的 IPV 病例。
在这项观察性队列研究中，我们从医院电子健康记录（EHR）中提取了非结构化医生和高级实践提供者、护理和社会工作者的记录。记录的临床记录和患者叙述被筛选出一套 23 个情境术语，这些术语来自有关 IPV 的文献（即配偶的攻击），以及一套 49 个扩展情境术语，这些术语来自已知的 IPV 病例（即配偶的攻击）。我们比较了所提出模型与 IPV 相关的国际疾病分类第 10 版代码检测的效果。
我们对 2012 年 1 月至 2020 年 8 月期间的总共 1,064,735 次患者就诊（405,303 名患者就诊于一级创伤中心的 ED）进行了分析。结果是确定是否存在与 IPV 相关的就诊。在这项研究中，我们使用嵌入在非结构化 EHR 数据中的信息来开发一种 NLP 算法，该算法使用临床记录来识别 ED 就诊的 IPV 访问。该算法使用了 23 个情境术语和 49 个扩展情境术语，成功识别了 7,399 次与 IPV 相关的就诊，代表 5,975 名患者；该算法在我们对 1,064,735 次 ED 就诊的样本中检测阳性病例的准确率达到了 99.5%。
使用一组预定义的与 IPV 相关的术语，我们成功开发了一种新的自然语言处理算法，能够识别亲密伴侣暴力。
