Data Driven Strategies for Substance Misuse Identification in Hospitalized Patients

住院患者药物滥用识别的数据驱动策略

• 批准号: 10265504
• 负责人: Majid Afshar
• 金额: $ 68.83万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-30 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10265504
• 关键词: Admission activity; Adopted; Adult; Alcohols; Artificial Intelligence; Benzodiazepines; Caring; Clinical; Clinical Data; Computing Methodologies; Consult; Costs and Benefits; Data; Data Set; Detection; Development; Documentation; Effectiveness; Electronic Health Record; Felis catus; General Population; Goals; Health Care Sector; Health system; Heart Diseases; Hospitalization; Hospitals; Hour; Illicit Drugs; Individual; Inpatients; Intake; Interruption; Intervention; Interviewer; Label; Learning; Light; Machine Learning; Manuals; Methods; Modeling; Modernization; Natural Language Processing; Patient Self-Report; Patients; Performance; Prevalence; Primary Health Care; Provider; Publishing; Questionnaires; Recommendation; Reference Standards; Research; Resources; Respiratory Failure; Risk; Risk Factors; Screening procedure; Semantics; Sensitivity and Specificity; Series; Social Work; Source; Standardization; Substance Abuse Detection; Testing; Text; Time; Training; Trust; Validation; Visit; addiction; alcohol misuse; alcohol use disorder; base; clinical decision support; cohort; comparison intervention; design; effectiveness evaluation; improved; individual patient; interoperability; machine learning method; multitask; non-opioid analgesic; novel; opioid misuse; polysubstance use; prospective; prospective test; response; routine care; screening; screening program; substance misuse; substance use; substance use treatment; supervised learning; support tools; tool; treatment as usual; trend; unstructured data

PROJECT SUMMARY The rate of substance use-related hospital visits in the US continues to increase, and now outpaces visits for heart disease and respiratory failure. The prevalence of substance misuse (nonmedical use of opioids and/or benzodiazepines, illicit drugs, and/or alcohol) in hospitalized patients is estimated to be 15%-25% and far exceeds the prevalence in the general population. With over 35 million hospitalized patients per year, tens of millions of patients are not screened for substance misuse during their stay. Despite the recommendation for self-report questionnaires (single-question universal screens, Alcohol Use Disorders Identification Test [AUDIT], Drug Abuse Screening Tool [DAST]), screening rates remains low in hospitals. Current screening methods are resource-intensive, so a comprehensive and automated approach to substance misuse screening that will augment current clinical workflow would therefore be of great utility. In the advent of Meaningful Use in the electronic health record (EHR), efficiency for substance misuse detection may be improved by leveraging data collected during usual care. Documentation of substance use is common and occurs in 97% of provider admission notes, but their free text format renders them difficult to mine and analyze. Natural Language Processing (NLP) and machine learning are subfields of artificial intelligence (AI) that provide a solution to analyze text data in the EHR to identify substance misuse. Modern NLP has fused with machine learning, another sub-field of AI focused on learning from data. In particular, the most powerful NLP methods rely on supervised learning, a type of machine learning that takes advantage of current reference standards to make predictions about unseen cases In our earlier version of an NLP and machine learning tool, our opioid and alcohol misuse classifiers successfully used data from clinical notes collected in the first 24 hours of hospital admission to reach a sensitivity and specificity above 75% for detecting alcohol or opioid misuse. We will improve the performance of our baseline, individual NLP single-substance classifiers for alcohol and opioid misuse by implementing multi-label and multi-task machine learning methods. These methods will take advantage of information shared across different types of substance misuse and better capture the state of a patient within a single model. The resulting classifier will be capable of jointly inferring all types of substance misuse (alcohol misuse, opioid misuse, and non-opioid illicit misuse) including polysubstance use, and cater to each individual patient’s substance use treatment needs. We aim to train and test our substance misuse classifiers at Rush in a retrospective dataset of over 35,000 hospitalizations that have been manually screened with the universal screen, AUDIT, and DAST. The top performing classifier will then be tested prospectively to: (1) externally validate its screening performance in a hospital without established screening; and (2) test its effectiveness against usual care at a hospital with questionnaire-based substance misuse screening. We hypothesize that a single-model NLP substance misuse classifier will provide a standardized, interoperable, and accurate approach for universal screening in hospitalized patients and guiding interventions.

项目摘要 美国与药物使用相关的医院就诊率不断增加，现在超过 访问心脏病和呼吸衰竭。药物滥用的患病率（非医学使用阿片类药物 和/或苯二氮卓，医院患者的非法药物和/或酒精估计为15％-25％， 远远超过了普通人群的患病率。每年有超过3500万住院的患者，数十个 在逗留期间，数百万患者没有被滥用。尽管建议 自我报告问卷（单个问题通用屏幕，酒精使用障碍识别测试 [审核]，药物滥用筛查工具[DAST]），医院的筛查率仍然很低。当前筛选 方法是资源密集的，因此是一种全面而自动化的滥用筛查的方法 因此，这将增加当前的临床工作流程将是极大的实用性。 在电子健康记录（EHR）中有意义地使用时，滥用物质的效率 利用在通常的护理过程中收集的数据可以改善检测。用途的文档是 常见并发生在97％的提供商的入学说明中，但它们的自由文本格式使它们难以 我的分析。自然语言处理（NLP）和机器学习是人造的子场 智能（AI）提供了一个解决方案来分析EHR中文本数据以识别物质滥用的解决方案。现代的 NLP与机器学习融合，这是AI的另一个子场，重点是从数据学习。特别是 最强大的NLP方法依赖于监督学习，一种机器学习，利用 当前的参考标准以对看不见的情况做出预测 在我们较早版本的NLP和机器学习工具中，我们的阿片类药物和酒精滥用分类器 成功使用了从住院的头24小时收集的临床笔记中的数据，以达到 对于检测酒精或阿片类药物滥用的敏感性和特异性超过75％。我们将提高性能 在我们的基线，单个NLP单一物质分类器，用于酒精和阿片类药物MISSUSE 多标签和多任务机器学习方法。这些方法将利用共享的信息 跨不同类型的物质滥用，并更好地捕获单个模型中患者的状态。这 由此产生的分类器将能够共同推断所有类型的药物滥用（滥用酒精，阿片类药物 滥用和非阿片类非法遗物），包括使用polysubstance，并迎合每个患者的 物质使用治疗需求。 我们的目标是在回顾性数据集中训练和测试滥用物质分类器 经过通用屏幕，审计和Dast手动筛查的35,000次住院治疗。这 然后，最佳性能分类器将被前瞻性测试：（1）外部验证其筛选性能 没有确定筛查的医院； （2）测试其针对医院通常护理的有效性 基于问卷的物质筛查。我们假设单模型NLP底物 滥用分类器将为普遍筛选提供标准化，可互操作和准确的方法 住院的患者和指导干预措施。