Homicides of Pregnant Women: Artificial Intelligence Detects Partner Violence and System Interaction.

Marguerite B Lucea, Andrea N Ramirez, Vijay Singh, Jacqueline C Campbell, Vinciya Pandian
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Abstract

Background: Homicide ranks among the top causes of pregnancy-associated mortality in the United States. Intimate partner violence (IPV) has been implicated in violent maternal deaths, before which pregnant women may interact with health care, law enforcement, and legal systems. Objective: To understand IPV and system engagement prior to maternal deaths and to test the viability of using artificial intelligence (AI) for the analysis of narratives, we compared AI and human-rater analyses of National Violent Death Reporting System Restricted Access Data (NVDRS-RAD) narratives for IPV circumstances and system interactions. Study Design: We conducted a secondary data analysis of the female homicide records in the 2018-2020 NVDRS-RAD narratives. We trained a bidirectional encoder representations from transformers (BERT) model on 5,082 female nonpregnant cases, validating it with the 351 pregnant or recently pregnant cases. We conducted AI performance metrics for sensitivity, specificity, precision, and kappa values, identified key terms, and compared AI with human-rater analyses. Results: Among 351 complete NVDRS narrative records of pregnant or postpartum female homicide victims, 285 had primary suspects identified. Human-rater and AI analysis identified similar numbers for whether the suspect was a current or former partner and whether IPV history was noted before homicide. Natural language processing (NLP)-identified word patterns highlighted differences between IPV and non-IPV cases. Human raters identified 24% (80/351), compared with NLP's identification of 21% (72/351), of pregnant women before death who interacted with health care and other systems. All AI models had strong performance metrics. Conclusions: Pregnant women in violent relationships interact with health care, law enforcement, and legal systems prior to their deaths. AI analysis is comparable with human raters in detecting IPV circumstances and system interactions among maternal homicides in the NVDRS. These findings highlight missed opportunities across sectors, underlining the importance of multisectoral interventions to prevent homicides of pregnant women.

孕妇凶杀案:人工智能检测伴侣暴力和系统互动。
背景:在美国,凶杀是导致妊娠相关死亡的主要原因之一。亲密伴侣暴力(IPV)与孕产妇暴力死亡有关,在此之前,孕妇可能与卫生保健、执法和法律系统发生互动。目的:为了了解孕产妇死亡前的IPV和系统参与情况,并测试使用人工智能(AI)进行叙事分析的可行性,我们比较了人工智能和人类对国家暴力死亡报告系统限制访问数据(NVDRS-RAD)关于IPV情况和系统交互的叙事的分析。研究设计:我们对2018-2020年NVDRS-RAD叙事中的女性杀人记录进行了二次数据分析。我们在5082名未怀孕的女性病例中训练了一个来自变压器(BERT)模型的双向编码器表示,并与351名怀孕或最近怀孕的病例进行了验证。我们对人工智能的灵敏度、特异性、精度和kappa值进行了性能度量,确定了关键术语,并将人工智能与人类分析进行了比较。结果:在351份完整的孕妇、产后女性凶杀被害人NVDRS叙事记录中,有285份确定了主要犯罪嫌疑人。对于嫌疑人是现任还是前任伴侣,以及在杀人前是否有IPV史,人类评估和人工智能分析得出了相似的数字。自然语言处理(NLP)识别的单词模式突出了IPV和非IPV情况之间的差异。与NLP鉴定的21%(72/351)相比,人类评分者鉴定出24%(80/351)的孕妇在死前与卫生保健和其他系统有过互动。所有的人工智能模型都有很强的性能指标。结论:处于暴力关系中的孕妇在死亡前与卫生保健、执法和法律系统相互作用。人工智能分析在检测NVDRS中孕产妇凶杀案的IPV情况和系统相互作用方面与人类评分者相当。这些调查结果突出了各部门错失的机会,强调了多部门干预措施对预防杀害孕妇的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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