Asthma ascertainment and characterization through electronic health records

 描述（由申请人提供）：哮喘是儿童中最常见的慢性疾病，也是美国五种负担最重的疾病之一。尽管如此，对儿童哮喘的流行病学调查受到不同地点哮喘诊断差异和电子病历（EMR）利用效率低下的限制，无法促进大规模研究。基于结构化数据的算法（例如，ICD-9编码）显示出很强的特异性，但缺乏基于人群的哮喘研究所需的敏感性。手动EMR检查允许应用公认的基于标准的定义，例如哮喘预测指数（API）或预先确定的哮喘标准（PAC），但劳动密集型且昂贵，因此对于人群水平研究不可行。由于缺乏一致的，可重复的，有效的哮喘确定方法，使用不一致的a。哮喘标准，B.确定过程，以及c.抽样框架导致临床试验或其他研究的哮喘组群和研究结果不一致。这种不一致性导致混淆，延迟将重要的研究结果转化为临床实践，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个强大的软件工具来简化基于哮喘标准（PAC和API）的哮喘自动医疗记录确定过程，从而推进哮喘的研究和临床护理。我们建议用自然语言处理（NLP）技术来增强传统的结构化数据标准，以解决非结构化文本的问题。因此，本提案的主要目标是开发NLP-API，一种用于自动化API的NLP算法，并将NLP算法应用于PAC和API以识别哮喘儿童队列。此外，我们将使用这些工具来描述哮喘儿童的特征，从而证明其在流行病学调查中的有用性，也可能在哮喘管理中。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，一致和有效地识别和表征哮喘状态。在目标1中，我们将开发NLP-API，一个用于API的NLP算法。在目标2中，我们将把NLP-API（在目标1下开发的）和NLP-PAC（我们最近开发的基于PAC的NLP算法）应用于两个评估队列。在目标3中，我们将通过评估NLP确定的哮喘状态与肺功能和哮喘生物标志物的相关性来描述目标2下确定的哮喘儿童亚组的特征。拟议研究的预期结果是：（i）通过实现更一致，可重复和有效的大规模哮喘确定，采样框架和时间估计来增强哮喘研究能力;（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;以及（iii）推进NLP技术在临床研究中的应用。该项目的成功完成将为解决儿童哮喘的重大负担提供准确，一致和有效的工具，并为扩展到其他慢性疾病和成人提供框架。

 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.