Enhanced Ascertainment of Asthma Status Via Natural Language Processing

 描述(申请人提供)：据估计，近一半的美国人患有慢性病，但流行病学调查受到难以大规模确定疾病状况的限制，即使在电子医疗记录(EMR)时代也是如此。例如，基于结构化数据(例如，ICD-9代码)的哮喘算法缺乏基于人群的研究所需的灵敏度，而对急诊医生的手动病历审查是劳动密集型的，因此对于人群规模的疾病状态确定效率低下。由于缺乏确定疾病状况的有效方法，严重限制了对哮喘等慢性病的调查范围。此外，患者的真实疾病状态有时间上的进展，这可能不会反映在该疾病的临床诊断中。我们之前曾报道，三分之二的哮喘儿童的诊断延迟(中位数：3.3年)，随后的病情，如缓解或复发，基本上没有报告。这种关于疾病进展的信息可以在手动病历审查期间记录下来，但同样，手动审查将调查和结论限制在小规模研究中。我们的长期目标是通过简化病历审查，加快对慢性病及其时间进展的流行病学调查。这项建议的主要目标是通过确定哮喘发作、缓解和复发的时间分类，扩展基于NLP的哮喘状态初步确定系统。我们将在人口健康环境中验证该系统，并将其作为开源工具发布。我们假设EMR中的NLP方法允许我们以比传统方法(帐单代码或人工病历审查)更高的准确性和效率来确定哮喘状态和跟踪哮喘疾病的进展。在目标1中，我们将扩展我们的初步NLP系统，以确定哮喘患者水平的疾病进展。最重要的是，我们将确定哮喘自然史上的两个重要事件--哮喘缓解和复发。我们还将改进聚合事件的方法，使用时态表达式和关系提取，包括结构化数据源，并实现自动特征选择。在目标2中，我们将评估NLP系统在确定哮喘发作、复发和缓解方面的准确性。我们还将对照现有研究验证流行病学(构建)的有效性，并将NLP系统作为开源项目ADEPT(患者时间表的疾病证据汇总)进行传播。预期结果：拟议的NLP系统将：(I)使临床NLP技术面向时间紧迫的患者水平的解决方案；(Ii)扩大哮喘研究能力的规模；以及(Iii)为决策支持和其他应用提供基础。该项目的成功完成将为确定哮喘的疾病进展提供一种开源工具，并提供一种汇总证据的一般方法。

 DESCRIPTION (provided by applicant): It is estimated that almost one-half of Americans suffer from chronic diseases, yet epidemiologic investigations are limited by the difficulty of ascertaining disease status at scale, even in the era of electronic medical records (EMRs). For example, algorithms based on structured data (e.g., ICD-9 codes) for asthma lack the sensitivity required for population-based studies, while manual medical record reviews of EMRs are labor-intensive and thus inefficient for population-scale ascertainment of disease status. The lack of efficient ways to ascertain disease status has severely restricted the scope of investigation for chronic diseases such as asthma. Furthermore, there is a temporal progression of a patient's true disease status, and this may not be reflected in the clinical diagnosis of that disease. We previously reported that two-thirds of children with asthma had a delay in their diagnosis (median: 3.3 years), with subsequent conditions like remission or relapse largely unreported. Such information about disease progression may be recorded during manual medical record review, but, again, manual review limits investigations and conclusions to small-scale studies. Our long term goal is to accelerate epidemiological investigations of chronic diseases and their temporal progression by streamlining medical record review. The main goal of this proposal is to extend a preliminary NLP-based system for asthma status ascertainment by identifying time-situated classifications of asthma onset, remission, and relapse. We will validate this system in a population health setting and release it as an open-source tool. We hypothesize that NLP methods in the EMR allow us to ascertain asthma status and to track asthma disease progression with greater accuracy and efficiency than conventional approaches (billing codes or manual medical record review). In Aim 1, we will extend our preliminary NLP system to ascertain the patient-level disease progression of asthma. Most significantly, we will ascertain time-situated asthma remission and relapse, two important events in the natural history of asthma. We will also improve methods of aggregating events, employ temporal expression and relation extraction, include structured data sources, and implement automatic feature selection. In Aim 2, we will evaluate the NLP system for its accuracy in ascertaining asthma onset, relapse, and remission. We will also verify the epidemiological (construct) validity against existing studies, and disseminate the NLP system as an open-source project, Adept (Aggregation of Disease Evidence for Patient Timelines). Expected Outcomes: The proposed NLP system will: (i) orient clinical NLP techniques toward time-situated patient-level solutions; (ii) expand the scale of research capabilities for asthma; and (iii) provide a basis for decision support and other applications. Successful completion of this project would provide an open-source tool for ascertaining the disease progression of asthma with a general approach to aggregating evidence.