de Boer Kathleen, Mackelprang Jessica L, Nedeljkovic Maja, Meyer Denny, Iyer Ravi
Department of Psychological Sciences, Swinburne University of Technology, Hawthorn, Australia.
JMIR Res Protoc. 2024 Dec 2;13:e54966. doi: 10.2196/54966.
Despite the implementation of prevention strategies, family violence continues to be a prevalent issue worldwide. Current strategies to reduce family violence have demonstrated mixed success and innovative approaches are needed urgently to prevent the occurrence of family violence. Incorporating artificial intelligence (AI) into prevention strategies is gaining research attention, particularly the use of textual or voice signal data to detect individuals at risk of perpetrating family violence. However, no review to date has collated extant research regarding how accurate AI is at identifying individuals who are at risk of perpetrating family violence.
The primary aim of this systematic review and meta-analysis is to assess the accuracy of AI models in differentiating between individuals at risk of engaging in family violence versus those who are not using textual or voice signal data.
The following databases will be searched from conception to the search date: IEEE Xplore, PubMed, PsycINFO, EBSCOhost (Psychology and Behavioral Sciences collection), and Computers and Applied Sciences Complete. ProQuest Dissertations and Theses A&I will also be used to search the grey literature. Studies will be included if they report on human adults and use machine learning to differentiate between low and high risk of family violence perpetration. Studies may use voice signal data or linguistic (textual) data and must report levels of accuracy in determining risk. In the data screening and full-text review and quality analysis phases, 2 researchers will review the search results and discrepancies and decisions will be resolved through masked review of a third researcher. Results will be reported in a narrative synthesis. In addition, a random effects meta-analysis will be conducted using the area under the receiver operating curve reported in the included studies, assuming sufficient eligible studies are identified. Methodological quality of included studies will be assessed using the risk of bias tool in nonrandomized studies of interventions.
As of October 2024, the search has not commenced. The review will document the state of the research concerning the accuracy of AI models in detecting the risk of family violence perpetration using textual or voice signal data. Results will be presented in the form of a narrative synthesis. Results of the meta-analysis will be summarized in tabular form and using a forest plot.
The findings from this study will clarify the state of the literature on the accuracy of machine learning models to identify individuals who are at high risk of perpetuating family violence. Findings may be used to inform the development of AI and machine learning models that can be used to support possible prevention strategies.
PROSPERO CRD42023481174; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=481174.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/54966.
尽管实施了预防策略，但家庭暴力仍是全球普遍存在的问题。当前减少家庭暴力的策略成效不一，迫切需要创新方法来预防家庭暴力的发生。将人工智能（AI）纳入预防策略正受到研究关注，特别是利用文本或语音信号数据来检测有实施家庭暴力风险的个体。然而，迄今为止，尚无综述整理有关人工智能在识别有实施家庭暴力风险个体方面的准确性的现有研究。
本系统综述和荟萃分析的主要目的是评估人工智能模型在利用文本或语音信号数据区分有实施家庭暴力风险的个体与无此风险的个体方面的准确性。
将从数据库建立到搜索日期对以下数据库进行搜索：IEEE Xplore、PubMed、PsycINFO、EBSCOhost（心理学和行为科学合集）以及计算机与应用科学全文数据库。ProQuest学位论文与学术期刊全文数据库也将用于搜索灰色文献。如果研究报告的是成年人群体，并使用机器学习来区分家庭暴力实施的低风险和高风险，则纳入该研究。研究可使用语音信号数据或语言（文本）数据，且必须报告确定风险时的准确性水平。在数据筛选、全文审查和质量分析阶段，两名研究人员将审查搜索结果，如有分歧，将通过第三名研究人员的盲审来解决。结果将以叙述性综述的形式呈现。此外，假设确定了足够数量的合格研究，将使用纳入研究中报告的受试者工作特征曲线下面积进行随机效应荟萃分析。将使用干预非随机研究中的偏倚风险工具评估纳入研究的方法学质量。
截至2024年10月，搜索尚未开始。该综述将记录有关人工智能模型利用文本或语音信号数据检测家庭暴力实施风险准确性的研究现状。结果将以叙述性综述的形式呈现。荟萃分析的结果将以表格形式并使用森林图进行总结。
本研究的结果将阐明关于机器学习模型识别有实施家庭暴力高风险个体准确性的文献现状。研究结果可用于为人工智能和机器学习模型的开发提供信息，这些模型可用于支持可能的预防策略。
PROSPERO CRD42023481174；https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=481174。
国际注册报告识别号（IRRID）：PRR1-10.2196/54966。
