Automating Delirium Identification and Risk Prediction in Electronic Health Records

抽象的。谵妄或急性意识模糊状态影响30-40%的住院老年人， 估计高达70亿美元。虽然最初被概念化为一种短暂性疾病，但谵妄现在 被认为具有重大后果，包括死亡风险增加、功能下降和长期 认知障碍由于高达75%的病例没有得到医疗服务提供者的认可， 用于临床和研究目的的确定谵妄的方法，以及基于谵妄风险对患者进行分层的方法。在 在这个提议中，我们提出了一种基于大规模数据挖掘的识别谵妄的新方法（即， 模式识别）算法，使用机器学习和自然语言处理应用于电子 健康记录（EHR）数据，这将自动化基于图表的谵妄状态和风险预测的确定。 我们将联合收割机与通过我们最近实施的虚拟急性护理收集的数据相结合， 老年人（ACE）质量改进项目，该项目由护理人员每班进行一次谵妄筛查， 所有年龄超过65岁的人都在亚拉巴马大学伯明翰分校（UAB）医院住院。这个意外- 减少的数据量将使我们能够实现必要的样本量，以进行有效的培训和验证 数据挖掘算法数据挖掘算法发现数据中的关联模式，而不是 测试预定的假设，非常适合应用于大规模的算法，用于识别 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，并发的医疗条件）， 与谵妄相关，将被归类为谵妄的风险因素，如体征、症状和描述符 谵妄本身，以及谵妄的并发症和后果，基于专家共识。然后我们将 使用这些特征来制定EHR中谵妄识别的规则，以及风险预测模型， 可以集成到EHR中，以提供谵妄风险的个性化评估。这项研究将奠定 在医疗保健环境中自动谵妄识别和风险预测方法的基础， 无法实施由提供者在我们的虚拟ACE中进行的筛查，以及大规模流行病学 使用EHR数据对谵妄进行调查，扩大了目前研究这种常见疾病的设备， 使人衰弱的疾病

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.