Adapting Natural Language Processing Tools for Biosurveillance

描述(由申请人提供)： 及早发现疾病暴发可以降低患者的发病率和死亡率，并最大限度地减少疾病的传播。早期发现需要在病程早期对患者的症状进行准确的分类。一种方法是生物监测，即在疾病过程的早期捕获电子症状数据，并分析可能表明疫情爆发的信号，需要公共卫生系统进行调查和应对。急诊科(ED)的患者记录对于生物监测特别有用，因为它们可以及时地以电子方式获得。监护系统中使用的急诊数据元素包括主诉(患者主要症状的简要描述(S))和分流护士笔记(也称为当前病史)。主诉是使用最广泛的急诊数据元素，因为大多数急诊都是以电子方式记录的。一项研究表明，增加分类记录提高了生物监测病例检测的敏感度。敏感性的提高是因为分诊记录增加了可用的数据量：分诊记录可能包含多种症状(例如，发烧、咳嗽和12小时的呼吸急促)，而不是主诉中的一个症状(例如发烧)。然而，由于急诊科主诉和分诊笔记中自由文本数据的广泛差异，监测工作受到阻碍。它们通常包括拼写错误、缩写、首字母缩写和其他难以分组到症状群中的词汇和语义变体(例如，发烧、温度104、FVR、发烧)。需要工具来解决ED数据中症状术语的词汇和语义差异，以改进生物监测。自然语言处理工具已被证明有助于从更结构化的临床数据(如放射学报告)中提取概念，但这些技术在自由文本ED分诊笔记中的应用有限。项目组开发了紧急医疗文本处理器(EMT-P)来对主要投诉进行预处理。EMT-P清理和标准化简要的主要投诉条目，然后提取标准化的概念，但在目前的状态下，它不足以对更长、更复杂的文本段落进行预处理，如分诊笔记。这一拟议项目将进一步加强生物监测，采用EMT-P和其他统计和经典自然语言处理工具，开发一个从分类笔记中提取概念的系统，用于生物监测。

DESCRIPTION (provided by applicant): Early detection of disease outbreaks can decrease patient morbidity and mortality and minimize the spread of diseases. Early detection requires accurate classification of patient symptoms early in the course of their illness. One approach is biosurveillance, in which electronic symptom data are captured early in the course of illness, and analyzed for signals that might indicate an outbreak requiring investigation and response by the public health system. Emergency department (ED) patient records are particularly useful for biosurveillance, given their timely, electronic availability. ED data elements used in surveillance systems include the chief complaint (a brief description of the patient's primary symptom(s)), and triage nurses' note (also known as history of present illness).The chief complaint is the most widely used ED data element, because it is recorded electronically by the majority of EDs. One study showed that adding triage notes increased the sensitivity of biosurveillance case detection. The increased sensitivity is because the triage note increases the amount of data available: instead of one symptom in a chief complaint (e.g., fever), triage notes may contain multiple symptoms (e.g., "fever, cough & shortness of breath for 12 hours"). Surveillance efforts are hampered, however, by the wide variability of free text data in ED chief complaints and triage notes. They often include misspellings, abbreviations, acronyms and other lexical and semantic variants that are difficult to group into symptom clusters (e.g., fever, temp 104, fvr, febrile). Tools are needed to address the lexical and semantic variation in symptom terms in ED data in order to improve biosurveillance. Natural language processing tools have been shown to facilitate concept extraction from more structured clinical data such as radiology reports, but there has been limited application of these techniques to free text ED triage notes. The project team developed the Emergency Medical Text Processor (EMT-P) to preprocess the chief complaint. EMT-P cleans and normalizes brief chief complaint entries and then extracts standardized concepts, but it is not sufficient in its current state to preprocess longer, more complex text passages such as triage notes. This proposed project will further strengthen biosurveillance by adapting EMT-P and other statistical and classical natural language processing tools to develop a system that extracts concepts from triage notes for biosurveillance.