Data Driven Strategies for Substance Misuse Identification in Hospitalized Patients

项目摘要 在美国，与药物使用相关的医院就诊率继续增加，现在已经超过了 心脏病和呼吸衰竭的病人药物滥用的流行率（阿片类药物的非医疗使用） 和/或苯二氮卓类药物、违禁药物和/或酒精）的比例估计为15%-25%， 远远超过了普通人群的患病率。每年有超过3500万住院患者， 数以百万计的病人在住院期间没有接受药物滥用筛查。尽管建议 自我报告问卷（单一问题通用筛选，酒精使用障碍识别测试 [AUDIT]，药物滥用筛查工具[DAST]），医院的筛查率仍然很低。目前的筛选 方法是资源密集型的，因此一种全面的自动化药物滥用筛查方法 因此，这将增强当前的临床工作流程，具有很大的实用性。 随着电子健康记录（EHR）中有意义使用的出现， 可以通过利用在常规护理期间收集的数据来改进检测。物质使用的文件是 常见，出现在97%的供应商录取通知书中，但其自由文本格式使其难以 挖掘和分析自然语言处理（NLP）和机器学习是人工智能的子领域。 智能（AI）提供了一种解决方案，用于分析EHR中的文本数据，以识别物质滥用。现代 NLP与机器学习融合，机器学习是人工智能的另一个子领域，专注于从数据中学习。特别是 大多数强大的NLP方法依赖于监督学习，这是一种利用 目前的参考标准，以预测未见过的情况 在我们早期版本的NLP和机器学习工具中，我们的阿片类和酒精滥用分类器 成功地使用了在入院的前24小时内收集的临床记录数据， 检测酒精或阿片类药物滥用的灵敏度和特异性高于75%。我们将改进性能 我们的基线，酒精和阿片类药物滥用的个人NLP单一物质分类器， 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，并在单个模型中更好地捕捉患者的状态。的 所得到的分类器将能够联合推断所有类型的物质滥用（酒精滥用、阿片类药物 滥用和非阿片类药物非法滥用），包括多种物质的使用，并满足每个患者的 物质使用治疗需要。 我们的目标是在Rush的回顾性数据集中训练和测试我们的物质滥用分类器， 35，000例住院患者已通过通用筛查、AUDIT和DAST进行手动筛查。的 然后将前瞻性地测试表现最好的分类器，以：（1）在外部验证其筛选性能， 没有建立筛选的医院;和（2）测试其有效性，与医院的常规护理相比， 基于汞的物质滥用筛查。我们假设一种单一模型的NLP物质 误用分类器将提供一个标准化的，可互操作的和准确的方法，用于通用筛选， 住院患者和指导干预措施。

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.