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

用于检测阿片类药物滥用的急诊科患者的机器学习方法

• 批准号: 10350200
• 负责人: Neeraj Chhabra
• 金额: $ 19.8万
• 依托单位: COOK COUNTY HEALTH AND HOSPITAL SYSTEM
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-15 至 2023-02-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10350200
• 关键词: Academic Medical Centers; Accident and Emergency department; Affect; Algorithms; American; Area; Biometry; COVID-19 pandemic; Cause of Death; Cessation of life; Characteristics; Clinical; Clinical Ethics; Cluster Analysis; Code; Cost Savings; County; Data; Data Science; Data Sources; Databases; Decision Trees; Detection; Development Plans; Diagnosis; Discrimination; Disease; Electronic Health Record; Emergency Care; Emergency Department patient; Emergency Department-based Intervention; Emergency Health Services; Ensure; Ethics; Evidence based intervention; Foundations; Fright; Future; Goals; Grant; Harm Reduction; Health; Hospitalization; Hour; Human; Hybrids; Individual; Institution; Intervention; Investigation; K-Series Research Career Programs; Link; Logistic Regressions; Machine Learning; Manuals; Measures; Medical; Mentored Patient-Oriented Research Career Development Award; Mentors; Mentorship; Methods; Modeling; Morbidity - disease rate; Natural Language Processing; Opioid; Outcome; Patient Care; Patient Self-Report; Patients; Performance; Process; Provider; Research; Research Personnel; Research Training; Resources; Risk; Role; Stigmatization; Systematic Bias; Techniques; Testing; Time; Toxicology; Training; United States; advanced analytics; base; career development; clinically relevant; cohort; cooking; deep neural network; improved; individual patient; innovation; machine learning algorithm; machine learning model; model building; mortality; multidisciplinary; multiple data sources; opioid misuse; opioid mortality; opioid overdose; opioid use disorder; patient engagement; patient oriented; patient-level barriers; predictive modeling; premature; prescription monitoring program; prevent; programs; prospective; research and development; screening; skills; social bias; substance use; targeted treatment

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.

项目摘要/摘要 阿片类药物的患者不成比例地利用紧急卫生服务，并有增加的风险 过早死亡。在紧急情况下对阿片类药物的及时准确识别 部门（ED）对于提供基于证据的干预措施以降低死亡率至关重要。阿片类药物面临的挑战 ED中的滥用检测包括提供者的时间限制，不一致的筛选方法和患者 自我报告的障碍。高级分析技术（例如机器学习和集群分析）提供 有效地表征和识别患有阿片类药物的患者在ED遇到的eD遇到的患者方面的承诺 利用电子健康记录（EHR）和处方药物监测计划（PDMP）中的数据。 使用多个数据源来识别阿片类药物MISSUSE的ED患者的机器学习方法的作用 尚未完全探索。在AIM 1中，使用ED遇到数据的多个机器学习算法将是 开发用于识别阿片类药物滥用。模型将对社会偏见进行系统评估 实施缓解策略以确保模型性能的公平性。在AIM 2中，包括纵向 将通过构建两者的模型来评估用于鉴定阿片类药物MISSUSE的ED患者的PDMP数据 使用集合堆叠方法的数据源。最后，在AIM 3中，无监督的潜在类别分析模型 将建造以识别ED患者滥用阿片类药物的临床相关亚表型，描述他们的 特征并确定面向患者的结果。用于检测ED患者的创新方法 使用多个数据源对机器学习模型进行严格测试，将使用阿片类药物的MISSUSE来追求 在临床部署之前进行社会偏见评估，并表征 阿片类药物滥用。这项受到指导的以患者为导向的职业发展奖的候选人（Neeraj博士 Chhabra）在急诊，医学毒理学，药物使用研究和 生物统计学。通过这个K23，他将进一步发展数据科学的技能，以建立全面可扩展 跨越多个数据域的模型，以鉴定阿片类药物滥用的患者。多学科 由他的主要导师（Niranjan Karnik博士）和联席会员（Majid Afshar博士，Harold博士）领导的指导团队 Pollack和Gail d'Onofrio博士）由在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。通过正式的综合计划 课程工作，道德培训，指导和研究，Chhabra博士将发展所需的技能 完成这些目标并过渡到独立调查。他的建议充分利用了 库克县健康和拉什大学医疗机构提供的合并资源 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 在ED环境中接受阿片类药物的患者。该K23奖为 追求这一目标，并将为评估这些模型的临床影响的未来R01提案构成基础。