Making the invisible visible: An automated clinical decision support tool for Intimate Partner Violence Risk and Severity Prediction (AIRS)

项目摘要 该项目的重点是开发一个自动化的临床决策支持工具，用于预测 基于历史成像和临床数据的亲密伴侣暴力（IPV）风险和严重程度。 尽管这一关键的公共卫生问题普遍存在，而且十分紧迫，但目前还没有 诊断IPV的客观工具。在卫生保健环境中检测IPV的挑战是 由于多种因素，包括患者的羞耻感和对后果的恐惧， 医生缺乏意识，害怕冒犯病人和伴侣。当成像发挥作用时， 在诊断儿童非意外创伤中的重要作用，因为明确的良好- 影像学研究的既定滥用模式，缺乏对IPV的循证研究 相关的成像模式导致IPV的识别不足和诊断不足。通过识别 当前和既往放射学研究中IPV的位置和成像模式， 放射科医生可以帮助识别IPV时，受害者不来。我们假设 多维临床支持工具，包括从 电子病历可以准确、全面地计算IPV风险。 然后可以集成自动化的IPV风险和严重程度预测，以改变护理 为幸存者制定计划，让“看不见”的东西变得可见。 目的1：通过分析IPV的放射学研究， 已知IPV存活者和匹配对照 目的2：通过开发临床决策支持来确定IPV风险和严重程度预测 从历史成像和临床预测因素中获得的工具。 目标3：在新数据集上验证IPV预测模型，并评估 导致放射学工作流程使用安全的储存库。 如果我们的假设是正确的，建立IPV相关的成像模式，CDS工具来自 整合到临床护理中的历史成像和临床预测因子将能够诊断IPV 客观地说

Project Abstract This project is focused on developing an automated clinical decision support tool for predicting Intimate Partner Violence (IPV) risk and severity based on historical imaging and clinical data. Despite the high prevalence and urgency of this critical public health issue, there is currently no objective tool to diagnose IPV. The challenges in detecting IPV in the health care setting are due to multiple factors, including the patient’s feelings of shame and fear of consequences and physician’s lack of awareness and fear of offending the patient and partner. While imaging plays an essential role in diagnosing nonaccidental trauma in children because of clear well- established patterns of abuse on imaging studies, a lack of evidence-based research on IPV related imaging patterns has led to under-recognition and underdiagnosis of IPV. By recognizing location and imaging patterns specific to IPV on current and previous radiological studies, radiologists can help identify IPV when the victims are not forthcoming. Our hypothesis is that a multidimensional clinical support tool including imaging and clinical findings harvested from the electronic medical record can provide an accurate and comprehensive calculation of IPV risk. The automated IPV risk and severity predictions can then be integrated to transform the care plan for survivors and make the “invisible” visible. Aim 1: To define IPV related imaging patterns and severity by analyzing radiological studies of known IPV survivors and matched controls Aim 2: To determine IPV risk and severity prediction by developing a clinical decision support tool derived from historical imaging and clinical predictors. Aim 3: To validate the IPV prediction model on new datasets and evaluate the integration of results in radiology workflow using a safe repository. If our hypotheses are correct, established IPV related imaging patterns, a CDS tool derived from historical imaging and clinical predictors integrated into clinical care will be able to diagnose IPV objectively.