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

项目摘要 该项目致力于开发用于预测的自动化临床决策支持工具 基于历史成像和临床数据的亲密伴侣暴力(IPV)风险和严重性。 尽管这一严重的公共卫生问题具有很高的普遍性和紧迫性，但目前没有 目的建立诊断传染性支气管炎的工具。在医疗保健环境中检测IPV面临的挑战是 由于多种因素，包括患者的羞耻感和对后果的恐惧，以及 医生缺乏意识，害怕冒犯患者和伴侣。当影像播放时 在诊断儿童非意外创伤中的重要作用，因为清楚的- 滥用成像研究的既定模式，缺乏对IPV的循证研究 相关的影像模式导致对IPV的认识不足和诊断不足。通过认识到 在当前和以前的放射学研究中针对IPV的位置和成像模式， 当受害者没有出现时，放射科医生可以帮助识别IPV。我们的假设是一个 多维临床支持工具，包括从 电子病历可以提供准确和全面的IPV风险计算。 然后，可以集成自动化的IPV风险和严重性预测，以转变医疗保健 为幸存者做好计划，让“看不见的”看得见。 目的1：通过分析IPV的放射学研究，明确IPV相关的成像模式和严重程度 已知的IPV幸存者和匹配的对照组 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 该工具源自历史成像和临床预测指标。 目的3：在新的数据集上验证IPV预测模型，并评估集成 导致使用安全存储库的放射科工作流程。 如果我们的假设是正确的，建立了与IPV相关的成像模式，CDS工具源自 集成到临床护理中的历史成像和临床预测将能够诊断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.