Asthma ascertainment and characterization through electronic health records

通过电子健康记录确定和表征哮喘

• 批准号: 8853379
• 负责人: YOUNG J JUHN
• 金额: $ 38.89万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-04-01 至 2018-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8853379
• 关键词: Accounting; Address; Adult; Algorithms; Asthma; Attention; Biological Markers; Birth; Caring; Child; Childhood; Childhood Asthma; Chronic; Chronic Disease; Clinic; Clinical; Clinical Data; Clinical Decision Support Systems; Clinical Research; Clinical Trials; Code; Cohort Studies; Computerized Medical Record; Computers; Confusion; Data; Data Set; Databases; Development; Diagnosis; Diagnostic; Disease; Electronic Health Record; Epidemiologic Studies; Epidemiology; Evaluation; Future; Genetic study; Goals; Gold; Heterogeneity; ICD-9; Information Retrieval; International Classification of Disease Codes; Investigation; Laboratories; Manuals; Medical Records; Methods; Morbidity - disease rate; Natural Language Processing; Outcome; Outcome Study; Patient Care; Patients; Play; Population; Process; Records; Reproducibility; Research; Research Personnel; Respiratory physiology; Risk; Role; Sampling; Sensitivity and Specificity; Site; Societies; Software Tools; Solutions; Specific qualifier value; Specificity; Structure; Subgroup; Symptoms; System; Techniques; Testing; Text; Time; Translational Research; Translations; United States; Variant; Work; base; clinical care; clinical practice; cohort; cost; evidence based guidelines; genome wide association study; improved; indexing; meetings; outcome forecast; population based; public health relevance; tool

 DESCRIPTION (provided by applicant): Asthma is the most common chronic condition in children and one of the five most burdensome disease in the United States. Despite this, epidemiologic investigations into childhood asthma are limited by variations in asthma diagnosis across sites and inefficient utilization of electronic medical records (EMRs) to facilitate large- scale studies. Algorithms based on structured data (e.g., ICD-9 codes) have shown strong specificity, but lack the sensitivity required for population-based studies for asthma. Manual EMR reviews allow application of well- recognized criteria-based definitions such as the Asthma Predictive Index (API) or the Predetermined Asthma Criteria (PAC), but are labor-intensive and expensive, and therefore not feasible for population-level studies. Because of the lack of consistent, reproducible, and efficient asthma ascertainment methods, the use of inconsistent a. asthma criteria, b. ascertainment processes, and c. sampling frames results in inconsistent asthma cohorts and study results for clinical trials or other studies. This inconsistency causes confusion, delayed translation of important study findings into clinical practice, and may obscure the true heterogeneity of asthma. Our long-term goal is to advance research and clinical care for asthma, by developing a robust software tool to streamline the process of automatic medical record ascertainment of asthma based on the asthma criteria (PAC and API). We propose to augment traditional structured data criteria with natural language processing (NLP) techniques to account for unstructured text. Thus, the main goal of this proposal is to develop NLP-API, an NLP algorithm for automating API, and apply the NLP algorithms for both PAC and API to identify a cohort of children with asthma. In addition, we will use the tools to characterize children with asthma thereby demonstrating its usefulness in epidemiological investigations and also possibly in asthma management. We hypothesize that asthma criteria-based NLP algorithms applied to the EMR will allow us to identify and characterize asthma status accurately, consistently, and efficiently. In Aim 1, we will develop NLP-API, an NLP algorithm for API. In Aim 2, we will apply both NLP-API (developed under Aim 1) and NLP-PAC (our recently developed PAC-based NLP algorithm) to two evaluation cohorts. In Aim 3, we will characterize the subgroups of children with asthma identified under Aim 2 by assessing the association of NLP-ascertained asthma status with lung function and biomarkers for asthma. The expected outcomes of the proposed study are: (i) enhanced research capabilities for asthma by enabling more consistent, reproducible, and efficient large-scale asthma ascertainment, sampling frames, and timing estimations; (ii) a basis for improving timely asthma diagnosis and care through clinical decision support systems; and (iii) advancement of the use of NLP techniques for clinical studies. Successful completion of this project will provide an accurate, consistent, and efficient tool for addressing the significant burden of asthma in children and a framework for extension to other chronic diseases and adults.

 描述（由适用提供）：哮喘是儿童中最常见的慢性病，​​也是美国五种最朴实的疾病之一。尽管如此，对儿童哮喘的流行病学研究受到跨站点哮喘诊断的变化以及电子病历（EMRS）的效率低下以促进大规模研究的限制。基于结构化数据（例如ICD-9代码）的算法显示出很强的特异性，但缺乏基于人群的哮喘研究所需的灵敏度。手动EMR综述允许应用良好的基于​​标准的定义，例如哮喘预测指数（API）或预定的哮喘标准（PAC），但劳动密集型且昂贵，因此对于人口水平的研究不可行。由于缺乏一致，可重复和有效的哮喘确定方法，因此使用不一致a。哮喘标准，b。确定过程，c。采样框架会导致哮喘同龄人不一致，并为临床试验或其他研究的研究结果。这种不一致会导致混乱，延迟将重要的研究发现转化为临床实践，并可能掩盖哮喘的真正异质性。我们的长期目标是通过开发一种强大的软件工具来简化基于哮喘标准（PAC和API）的自动病历确定哮喘的过程，以促进哮喘的研究和临床护理。我们建议使用自然语言处理（NLP）技术增强传统的结构化数据标准，以说明非结构化的文本。这是该提案的主要目标是开发用于自动化API的NLP算法NLP-API，并将NLP算法应用于PAC和API，以识别一群患有哮喘的儿童。此外，我们将使用这些工具来表征患有哮喘的儿童，从而证明了其在流行病学研究中的有用性，并且在哮喘管理中也可能存在。我们假设基于哮喘标准的NLP算法应用于EMR，将使我们能够准确，一致，一致，有效地识别和表征哮喘状态。在AIM 1中，我们将开发API的NLP-API，一种NLP算法。在AIM 2中，我们将同时将NLP-API（根据AIM 1）和NLP-PAC（我们最近开发的基于PAC的NLP算法）应用于两个评估群体。在AIM 3中，我们将通过评估NLP确定的哮喘状况与肺功能和哮喘生物标志物的关联来表征AIM 2下发现哮喘儿童的亚组。拟议的研究的预期结果是：（i）通过实现更一致，可重现和有效的大规模哮喘确定，抽样框架和时机估计来增强哮喘的研究能力； （ii）通过临床决策支持系统改善及时哮喘诊断和护理的基础； （iii）使用NLP技术用于临床研究的发展。该项目的成功完成将提供一种准确，一致和有效的工具，以解决儿童哮喘的大量燃烧以及向其他慢性疾病和成人扩展的框架。