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

• 批准号: 8995191
• 负责人: YOUNG J JUHN
• 金额: $ 23.85万
• 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: 2015-01-15 至 2017-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8995191
• 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; Health; 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; Specificity; Structure; System; Techniques; Testing; Text; Time; TimeLine; Work; base; clinical Diagnosis; clinical care; clinical practice; cohort; improved; learning strategy; open source; population based; population health; response; tool

 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（申请人提供）：据估计，近一半的美国人患有慢性病，但即使在电子病历（EMR）时代，流行病学调查也因难以大规模确定疾病状况而受到限制。例如，基于哮喘结构化数据（例如 ICD-9 代码）的算法缺乏基于人群的研究所需的敏感性，而 EMR 的手动医疗记录审查是劳动密集型的，因此对于人群规模的疾病状态查明效率低下。缺乏确定疾病状态的有效方法严重限制了哮喘等慢性病的调查范围。此外，患者的真实疾病状态存在时间进展，这可能无法反映在该疾病的临床诊断中。我们之前报道过，三分之二的哮喘儿童的诊断延迟（中位数：3.3 年），随后的病情缓解或复发等情况基本上没有报告。有关疾病进展的此类信息可能会在手动病历审查期间记录，但手动审查再次将调查和结论限制于小规模研究。我们的长期目标是通过简化病历审查来加速慢性病及其时间进展的流行病学调查。该提案的主要目标是通过确定哮喘发作、缓解和复发的时间分类来扩展基于 NLP 的初步哮喘状态确定系统。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 方法使我们能够比传统方法（账单代码或手动病历审核）更准确、更高效地确定哮喘状态并跟踪哮喘疾病进展。在目标 1 中，我们将扩展我们的初步 NLP 系统，以确定哮喘患者的疾病进展。最重要的是，我们将确定哮喘缓解和复发的时间定位，这是哮喘自然史中的两个重要事件。我们还将改进聚合事件的方法，采用时间表达和关系提取，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。我们还将根据现有研究验证流行病学（构建）有效性，并将 NLP 系统作为开源项目 Adept（患者时间线疾病证据聚合）进行传播。预期成果：拟议的 NLP 系统将：（i）将临床 NLP 技术定位于特定时间的患者级解决方案； (ii) 扩大哮喘研究能力的规模； (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通过收集证据的通用方法来确定哮喘疾病进展提供一个开源工具。