Machine learning approaches for the detection of emergency department patients with opioid misuse

项目摘要/摘要 滥用阿片类药物的患者不成比例地使用紧急医疗服务，并面临更高的风险 过早死亡。急诊阿片类药物滥用患者的及时准确识别 部门(ED)对于提供循证干预措施以降低死亡率至关重要。阿片类药物面临的挑战 急诊室的误用检测包括提供者的时间限制、不一致的筛查方法和患者 自我报告的障碍。机器学习和聚类分析等高级分析技术可提供 承诺通过以下方式有效地鉴定和识别在ED遭遇期间滥用阿片类药物的患者 利用电子健康记录(EHR)和处方药监测计划(PDMP)中的数据。 利用多种数据源的机器学习方法在识别滥用阿片类药物的ED患者中的作用 尚未得到充分的探索。在目标1中，使用ED遭遇数据的多种机器学习算法将是 为识别阿片类药物滥用而开发的。将系统地评估模特的社会偏见和 实施缓解战略，以确保模型性能的公平性。在目标2中，列入了纵向 用于识别滥用阿片类药物的ED患者的PDMP数据将通过建立两个模型进行评估 使用集成堆叠方法的数据源。最后，在目标3中，提出了一种无监督的潜在类分析模型 将建立以识别临床相关的阿片类药物滥用的ED患者的亚型，描述他们的 特征，并决定以患者为导向的结果。一种检测ED患者的创新方法 将通过严格测试利用多个数据源的机器学习模型来追踪阿片类药物滥用， 在临床部署之前进行社会偏见评估，并描述潜在的患者群体 阿片类药物滥用。这项以患者为导向的指导职业发展奖的候选人(Neeraj博士 Chhabra)在急救、医疗毒理学、物质使用研究以及 生物统计学。通过这一K23，他将进一步发展数据科学方面的技能，以构建全面和可扩展的 用于识别阿片类药物滥用患者的跨越多个数据域的模型。多学科的 由他的主要导师(Niranjan Karnik博士)和共同导师(Majid Afshar博士、Harold博士)领导的导师团队 波拉克和盖尔·多诺弗里奥博士)由物质使用研究领域的全国知名专家组成， 机器学习、自然语言处理和临床伦理学。通过一个正式的 课程、道德培训、指导和研究，Chhabra博士将发展必要的技能集 完成这些目标并过渡到独立调查。他的建议充分利用了 库克县卫生和拉什大学医学院附属机构提供的综合资源 中心。Chhabra博士的长期目标是利用机器学习技术来集中治疗和资源 对于在急诊室内滥用阿片类药物的患者。这项K23大奖提供了必要的基础 追求这一目标，并将形成未来R01提案的基础，评估这些模型的临床影响。

Project Summary/Abstract Patients with opioid misuse disproportionately utilize emergency health services and are at increased risk for premature death. The timely and accurate identification of patients with opioid misuse in the Emergency Department (ED) is critical to provide evidence-based interventions to decrease mortality. Challenges to opioid misuse detection in the ED include provider time constraints, inconsistent screening approaches, and patient barriers to self-reporting. Advanced analytic techniques such as machine learning and cluster analyses offer promise in efficiently characterizing and identifying patients with opioid misuse during their ED encounter by leveraging data within the electronic health record (EHR) and the prescription drug monitoring program (PDMP). The role of machine learning approaches utilizing multiple data sources to identify ED patients with opioid misuse has yet to be fully explored. In aim 1, multiple machine learning algorithms using ED encounter data will be developed for the identification of opioid misuse. Models will be systematically assessed for social biases and mitigation strategies implemented to ensure equity in model performance. In aim 2, the inclusion of longitudinal PDMP data for the identification of ED patients with opioid misuse will be evaluated by building models from both data sources utilizing ensemble stacking methods. Finally, in aim 3, an unsupervised latent class analysis model will be built to identify clinically relevant subphenotypes of ED patients with opioid misuse, describe their characteristics, and determine patient-oriented outcomes. An innovative approach to the detection of ED patients with opioid misuse will be pursued by rigorously testing machine learning models utilizing multiple data sources, conducting social bias assessments prior to clinical deployment, and characterizing latent groups of patients with opioid misuse. The candidate for this Mentored Patient-Oriented Career Development Award (Dr. Neeraj Chhabra) possesses a strong foundation in emergency care, medical toxicology, substance use research, and biostatistics. Through this K23, he will further develop skills in data science to build comprehensive and scalable models spanning multiple data domains for the identification of patients with opioid misuse. The multidisciplinary mentorship team led by his primary mentor (Dr. Niranjan Karnik) and co-mentors (Dr. Majid Afshar, Dr. Harold Pollack, and Dr. Gail D’Onofrio) consists of nationally renowned experts in the fields of substance use research, machine learning, natural language processing, and clinical ethics. Through an integrated program of formal coursework, ethics training, mentorship, and research, Dr. Chhabra will develop the skillset necessary to complete these aims and transition to independent investigation. His proposal takes full advantage of the combined resources provided by the affiliated institutions of Cook County Health and Rush University Medical Center. Dr. Chhabra’s long-term goal is to utilize machine learning techniques to focus treatments and resources towards patients with opioid misuse within the ED setting. This K23 award provides the necessary foundation to pursue this goal and will form the basis for future R01 proposals evaluating the clinical impact of these models.