Enhanced Ascertainment of Asthma Status Via Natural Language Processing

通过自然语言处理增强哮喘状态的确定

基本信息

批准号：
8860691
负责人：
YOUNG J JUHN
金额：
$ 19.88万
依托单位：
MAYO CLINIC ROCHESTER
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-01-15 至 2016-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8860691
关键词：
Accounting Address Algorithms American Asthma Atopic Dermatitis Caring Child Chronic Chronic Disease Classification Clinical Clinical Research Code Computerized Medical Record Data Data Set Data Sources Diagnosis Disease Disease Progression Disease remission Epidemiologic Studies Epidemiology Ethnic Origin Event Future Generations Goals Gold ICD-9 Infection Institution Investigation Logic Machine Learning Manuals Medical Records Methods Natural History Natural Language Processing Outcome Patient Care Patients Population Predictive Value Public Health Reading Relapse Reporting Research Risk Sensitivity and Specificity Solutions Specificity Structure System Techniques Testing Text Time TimeLine Work base clinical Diagnosis clinical care clinical practice cohort improved open source population based population health public health relevance response tool

项目摘要

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.

描述（由适用提供）：据估计，几乎一半的美国人患有慢性疾病，但是流行病学研究受到确定规模确定疾病状况的困难，即使在电子病历时代（EMRS）也受到限制。例如，基于结构化数据（例如，ICD-9代码）的算法缺乏基于人群研究所需的敏感性，而手动医疗记录的EMR审查却是劳动力密集型的，因此对疾病状况的人口规模确定性效率低下。缺乏确定疾病状况的有效方法严重限制了诸如哮喘等慢性疾病的调查范围。此外，患者的真实疾病状况暂时进展，这可能不会反映在该疾病的临床诊断中。我们以前报道说，哮喘患有诊断的三分之二的儿童（中位数：3。3年）延迟了，随后的情况如缓解或继电器等条件很大程度上未报告。有关疾病进展的此类信息可以在手动病历审查期间记录，但同样，手动审查限制了小型研究的调查和结论。我们的长期目标是通过简化病历审查来加速慢性疾病的流行病学研究及其临时进展。该提案的主要目标是通过识别哮喘发作，缓解，缓解和将其释放为开源工具，扩展哮喘状态确定的基于初步的NLP系统。我们假设EMR中的NLP方法使我们能够确定哮喘状况并以比常规方法（计费代码或手动记录）审查更高的准确性和效率跟踪哮喘疾病进展）。在AIM 1中，我们将扩展初步的NLP系统，以确定哮喘的患者水平疾病进展。最重要的是，我们将确定时间定义的哮喘缓解和缓解，这是哮喘自然史上的两个重要事件。我们还将改善汇总事件，员工临时表达和关系提取的方法，包括结构化数据源，并实现自动特征选择。在AIM 2中，我们期望的结果是：拟议的NLP系统将：（i）与时间固定的患者级解决方案的方向临床NLP技术；（ii）扩大哮喘研究能力的规模；（iii）为决策支持和其他申请提供了基础。该项目的成功完成将为确定哮喘的疾病进展提供开源工具，并采用总体证据的一般方法。