Survivors of child sex trafficking (SCST) experience high rates of adverse health outcomes. Amidst the duration of their victimization, survivors regularly seek healthcare yet fail to be identified. This study sought to utilize artificial intelligence (AI) to identify SCST and describe the elements of their healthcare presentation. An AI-supported keyword search was conducted to identify SCST within the electronic medical records (EMR) of ∼1.5 million patients at a large midwestern pediatric hospital. Descriptive analyses were used to evaluate associated diagnoses and clinical presentation. A sex trafficking-related keyword was identified in .18% of patient charts. Among this cohort, the most common associated diagnostic codes were for Confirmed Sexual/Physical Assault; Trauma and Stress-Related Disorders; Depressive Disorders; Anxiety Disorders; and Suicidal Ideation. Our findings are consistent with the myriad of known adverse physical and psychological outcomes among SCST and illuminate the future potential of AI technology to improve screening and research efforts surrounding all aspects of this vulnerable population.

中文翻译：

---

从电子健康记录中识别性贩运儿童幸存者：人工智能引导方法。

儿童性贩运 (SCST) 的幸存者遭受不良健康后果的比例很高。在受害期间，幸存者经常寻求医疗保健，但身份不明。本研究试图利用人工智能 (AI) 来识别 SCST 并描述其医疗保健演示的要素。我们进行了人工智能支持的关键字搜索，以识别中西部一家大型儿科医院约 150 万名患者的电子病历 (EMR) 中的 SCST。描述性分析用于评估相关诊断和临床表现。在 0.18% 的患者图表中发现了与性交易相关的关键词。在该队列中，最常见的相关诊断代码是已确认的性/身体攻击；创伤和压力相关疾病；抑郁症；焦虑症; 和自杀意念。我们的研究结果与 SCST 中已知的无数不良生理和心理结果一致，并阐明了人工智能技术在改善围绕这一弱势群体各个方面的筛查和研究工作方面的未来潜力。