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

基本信息

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
项目类别：
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

项目摘要

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。谵妄或急性精神错乱状态影响着 30-40% 的住院老年人，并增加了费用护理费用估计高达 70 亿美元。虽然谵妄最初被概念化为一种短暂性障碍，但现在已被定义为公认会产生重大后果，包括死亡风险增加、功能衰退和长期认知障碍。由于高达 75% 的病例未被提供商认可，因此迫切需要额外的用于临床和研究目的识别谵妄的方法，以及根据谵妄风险对患者进行分层的方法。在在这项提案中，我们提出了一种基于大规模数据挖掘（即，模式识别）使用机器学习和自然语言处理的算法应用于电子健康记录（EHR）数据，它将自动基于图表确定谵妄状态和风险预测。我们将把这些算法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次进行一次谵妄筛查所有 65 岁以上的人均入住阿拉巴马大学伯明翰分校 (UAB) 医院。这前所未有—— 减少的数据量将使我们能够达到有效训练和验证所需的样本量我们的数据挖掘算法。数据挖掘算法发现数据中的关联模式，而不是测试预先确定的假设，非常适合用于识别的大规模算法中的应用谵妄。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、并发医疗状况）作为- 与谵妄相关，将被归类为谵妄的危险因素，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。我们随后将使用这些功能来开发 EHR 中谵妄的识别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。这项研究将奠定为医疗保健环境中自动谵妄识别和风险预测方法奠定了基础无法实施由提供商在我们的 Virtual ACE 中进行的筛查以及大规模的流行病学筛查使用 EHR 数据对谵妄进行调查，扩大了当前研究这种常见和使人衰弱的疾病。