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: 10608099
• 负责人: Neeraj Chhabra
• 金额: $ 19.93万
• 依托单位: UNIVERSITY OF ILLINOIS AT CHICAGO
• 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 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10608099
• 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; Equity; Ethics; Evidence based intervention; Foundations; Fright; Future; Goals; Grant; Harm Reduction; Health; Hospitalization; Hour; Human; 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; Patient-Focused Outcomes; Patients; Performance; Phenotype; Process; Provider; Research; Research Personnel; Resources; Risk; Role; Stigmatization; Systematic Bias; Techniques; Testing; Time; Toxicology; Training; United States; advanced analytics; 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.

项目概要/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，并且面临更大的风险 紧急情况下及时准确地识别阿片类药物滥用患者的过早死亡。 部门 (ED) 对于提供循证干预措施以降低阿片类药物的死亡率至关重要。 急诊室中的误用检测包括提供者的时间限制、不一致的筛查方法以及患者 机器学习和聚类分析等先进分析技术提供了障碍。 有望有效地描述和识别在 ED 就诊期间阿片类药物滥用的患者 利用电子健康记录 (EHR) 和处方药监测计划 (PDMP) 中的数据。 机器学习方法利用多个数据源识别阿片类药物滥用的 ED 患者的作用 在目标 1 中，将使用 ED 遭遇数据的多种机器学习算法尚未得到充分探索。 为识别阿片类药物滥用而开发的模型将系统地评估社会偏见和情况。 为确保模型性能公平而实施的缓解策略在目标 2 中包含纵向数据。 将通过建立模型来评估用于识别阿片类药物滥用的 ED 患者的 PDMP 数据 最后，在目标 3 中，使用无监督的潜在类分析模型。 将建立以确定阿片类药物滥用的 ED 患者的临床相关亚表型，描述他们的情况 特征，并确定以患者为中心的结果，这是一种检测 ED 患者的创新方法。 对于阿片类药物的滥用，将通过利用多个数据源严格测试机器学习模型来解决， 在临床部署之前进行社会偏见评估，并描述潜在患者群体的特征 阿片类药物滥用。该以患者为导向的职业发展奖的候选人（Neeraj 博士） Chhabra）在急救护理、医学毒理学、物质使用研究和 通过这个 K23，他将进一步发展数据科学技能，以构建全面且可扩展的数据科学。 跨越多个数据领域的模型，用于识别阿片类药物滥用患者。 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 Pollack 和 Gail D’Onofrio 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正式的综合项目。 课程作业、道德培训、指导和研究，查布拉博士将培养必要的技能 他的建议充分利用了完成这些目标并过渡到独立调查的机会。 库克县卫生局和拉什大学医学院附属机构提供的综合资源 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。 该 K23 奖项为在急诊室滥用阿片类药物的患者提供了必要的基础。 追求这一目标并将成为未来评估这些模型临床影响的 R01 提案的基础。