Automating Delirium Identification and Risk Prediction in Electronic Health Records

抽象的。精神错乱或急性昏迷状态影响着30%-40%的住院老年人，并增加了费用 医疗费用估计高达70亿美元。虽然最初的概念是一种暂时性的障碍，但精神错乱现在是 公认具有重大后果，包括死亡风险增加、功能衰退和长期 认知障碍。由于高达75%的案例无法被提供商识别，因此迫切需要额外的 方法识别临床和研究目的的妄想，并根据妄想的风险对患者进行分层。在……里面 在此基础上，我们提出了一种新的基于大规模数据挖掘的精神错乱识别方法(即， 使用机器学习和自然语言处理的算法在电子产品中的应用 健康记录(EHR)数据，这将自动化基于图表的精神错乱状态和风险预测的确定。 我们将把这些算法与我们最近实施的虚拟急救服务收集的数据结合起来 老年人(ACE)质量改进项目，该项目规定护理人员每班进行一次精神错乱筛查 所有65岁以上的人都住进了阿拉巴马大学伯明翰分校(UAB)医院。这件事的揭开- 减少数据量将使我们能够获得有效培训和验证所需的样本量 我们的数据挖掘算法。发现数据中关联模式的数据挖掘算法，而不是 测试预定的假设，非常适合应用于大规模算法中的识别 精神错乱。使用我们的虚拟ACE和医院EHR数据，我们将能够评估10,000多名个人 特征(例如，文本单词和短语、实验室和其他诊断测试、并发医疗条件)如下- 与精神错乱有关，这将被归类为精神错乱的危险因素，如体征、症状和描述符。 根据专家的共识，精神错乱本身以及精神错乱的并发症和后果。到时候我们会的 使用这些功能来开发识别EHR中的精神错乱的规则，以及风险预测模型 可以集成到EHR中，以提供对精神错乱风险的个性化评估。这项研究将为 医疗保健环境中的自动精神错乱识别和风险预测方法的基础 无法实施供应商在我们的虚拟ACE中进行的筛查，以及大规模流行病学 用EHR资料研究妄想症，扩充目前研究这种常见疾病的仪器。 使人衰弱的紊乱。

Abstract. Delirium, or acute confusional state, affects 30-40% of hospitalized older adults, with the added cost of care estimated to be up to $7 billion. Although originally conceptualized as a transient disorder, delirium is now recognized to have significant consequences, including increased risk of death, functional decline, and long-term cognitive impairment. As up to 75% cases are not recognized by providers, there is an urgent need for additional methods to identify delirium for clinical and research purposes, and to stratify patients based on delirium risk. In this proposal, we present a novel approach to the identification of delirium based on large-scale data mining (i.e., pattern recognition) algorithms using machine learning and natural language processing applied to electronic health record (EHR) data, which will automate chart-based determination of delirium status and risk prediction. We will combine these algorithms with data collected through our recently implemented Virtual Acute Care for Elders (ACE) quality improvement project, which institutes delirium screening once per shift by nursing staff for all individuals over age 65 admitted to the University of Alabama at Birmingham (UAB) Hospital. This unprece- dented volume of data will allow us to achieve the necessary sample sizes for effective training and validation of our data mining algorithms. Data mining algorithms that discover patterns of associations in data, rather than testing predetermined hypotheses, are well suited to application in large-scale algorithms for identification of delirium. Using our Virtual ACE and hospital EHR data, we will be able to evaluate more than 10,000 individual features (e.g., text words and phrases, laboratory and other diagnostic tests, concurrent medical conditions) as- sociated with delirium, which will be classified as risk factors for delirium, as signs, symptoms, and descriptors of delirium itself, and as complications and consequences of delirium, based on expert consensus. We will then use these features to develop rules for identification of delirium in the EHR, as well as risk prediction models that can be integrated into the EHR to provide individualized assessments of delirium risk. This study will lay the foundation for methods of automated delirium identification and risk prediction in healthcare settings that are unable to implement the screening by providers done in our Virtual ACE, as well as for large-scale epidemiological investigations of delirium using EHR data, expanding the current armamentarium for studying this common and debilitating disorder.