Automating Delirium Identification and Risk Prediction in Electronic Health Records

电子健康记录中谵妄的自动化识别和风险预测

• 批准号: 10091381
• 负责人: RICHARD E KENNEDY
• 金额: $ 37.69万
• 依托单位: UNIVERSITY OF ALABAMA AT BIRMINGHAM
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-15 至 2022-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10091381
• 关键词: Acute; Address; Adult; Affect; Agreement; Alabama; Algorithms; Ally; Assessment tool; Automation; Caring; Characteristics; Clinical Research; Cognition; Cognitive; Computational algorithm; Confusion; Consensus; Data; Data Analyses; Data Set; Delirium; Descriptor; Detection; Development; Diagnostic tests; Discipline of Nursing; Disease; Elderly; Electronic Health Record; Epidemiology; Foundations; Growth; Health system; Hospitals; Impaired cognition; Individual; Inpatients; Institutes; Institutionalization; Laboratories; Link; Logistics; Long-Term Care for Elderly; Machine Learning; Measurable; Medical; Methods; Modeling; Monitor; Natural Language Processing; Nurses; Nursing Staff; Operative Surgical Procedures; Patients; Pattern; Pattern Recognition; Persons; Prevention; Property; Provider; ROC Curve; Reference Standards; Research; Resources; Risk; Risk Factors; Sample Size; Sampling; Signs and Symptoms; Testing; Text; Time; Training; Universities; Validation; acute care; adverse outcome; base; care costs; confusion assessment method; data mining; epidemiology study; functional decline; functional disability; health care settings; high dimensionality; human old age (65+); improved; instrument; interest; large scale data; model development; mortality risk; novel; novel strategies; patient stratification; phrases; prediction algorithm; programs; risk prediction; risk prediction model; screening; validation studies; virtual; ward

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.

抽象的。 del妄或急性混乱状态会影响30-40％的住院老年人，增加了费用 估计高达70亿美元的护理。尽管最初被概念化为短暂疾病，但现在是 公认会产生重大后果，包括增加死亡风险，功能下降和长期 认知障碍。由于提供者未认识到多达75％的案件，因此迫切需要额外 识别用于临床和研究目的del妄的方法，并根据ir妄风险对患者进行分层。在 这项建议，我们提出了一种基于大规模数据挖掘的del妄的新方法（即 模式识别）使用机器学习和应用于电子的自然语言处理算法 健康记录（EHR）数据将自动化基于图表的ir妄状态和风险预测的确定。 我们将将这些算法与通过最近实施的虚拟急性护理收集的数据相结合 长者（ACE）质量改进项目，该项目每班一次del妄筛查一次 所有65岁以上的人都被伯明翰（UAB）医院的阿拉巴马大学入院。这个无与伦比的 凹痕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 我们的数据挖掘算法。数据挖掘算法发现数据中关联的模式，而不是 测试预定的假设非常适合用于大规模算法以识别 谵妄。使用我们的虚拟ACE和医院EHR数据，我们将能够评估10,000多个个人 特征（例如，文本和短语，实验室和其他诊断测试，同时医疗状况） 通过del妄社交，将被归类为ir妄的风险因素，作为符号，症状和描述符 基于专家共识的del妄本身，以及del妄的并发症和后果。然后我们会 使用这些功能来制定识别EHR中ir妄的规则，以及风险预测模型 可以集成到EHR中，以提供对ir妄风险的个性化评估。这项研究将为 自动化ir妄识别和风险预测方法的基础 无法实施我们虚拟ACE和大规模流行病学的提供者的筛选 使用EHR数据研究ir妄的调查，扩大了目前的arammentarium，以研究这种常见和 使人衰弱的障碍。