Marguerite B Lucea, Andrea N Ramirez, Vijay Singh, Jacqueline C Campbell, Vinciya Pandian
{"title":"孕妇凶杀案:人工智能检测伴侣暴力和系统互动。","authors":"Marguerite B Lucea, Andrea N Ramirez, Vijay Singh, Jacqueline C Campbell, Vinciya Pandian","doi":"10.1177/15409996251380353","DOIUrl":null,"url":null,"abstract":"<p><p><b><i>Background:</i></b> 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. <b><i>Objective:</i></b> 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. <b><i>Study Design:</i></b> 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. <b><i>Results:</i></b> 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. <b><i>Conclusions:</i></b> 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.</p>","PeriodicalId":520699,"journal":{"name":"Journal of women's health (2002)","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Homicides of Pregnant Women: Artificial Intelligence Detects Partner Violence and System Interaction.\",\"authors\":\"Marguerite B Lucea, Andrea N Ramirez, Vijay Singh, Jacqueline C Campbell, Vinciya Pandian\",\"doi\":\"10.1177/15409996251380353\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p><b><i>Background:</i></b> 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. <b><i>Objective:</i></b> 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. <b><i>Study Design:</i></b> 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. <b><i>Results:</i></b> 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. <b><i>Conclusions:</i></b> 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.</p>\",\"PeriodicalId\":520699,\"journal\":{\"name\":\"Journal of women's health (2002)\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-09-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of women's health (2002)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/15409996251380353\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of women's health (2002)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/15409996251380353","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Homicides of Pregnant Women: Artificial Intelligence Detects Partner Violence and System Interaction.
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.