Adapting Natural Language Processing Tools for Biosurveillance

采用自然语言处理工具进行生物监测

基本信息

批准号：
8144459
负责人：
DEBBIE A TRAVERS
金额：
$ 14.72万
依托单位：
UNIV OF NORTH CAROLINA CHAPEL HILL
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-09-28 至 2013-04-27
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8144459
关键词：
Abbreviations Accident and Emergency department Acute Address American Avian Influenza Bioterrorism Bird Flu vaccine Breathing Classification Clinical Clinical Data Clinical Research Code Collection Communicable Diseases Communities Complex Contracts Coughing Country Data Data Element Data Quality Detection Diagnosis Disease Disease Outbreaks Early Diagnosis Electronic Health Record Electronics Emergency Medicine Emergency Situation Emerging Communicable Diseases Epidemiologist Epidemiology Evaluation Event Fever Genetic Transcription Goals Gold Health Health Services Health system Hour Intervention Investigation Manuals Measures Medical Methods Monitor Morbidity - disease rate Natural Language Processing North Carolina Nurses Patients Performance Pertussis Physicians Predictive Value Process Public Health Radiology Specialty Recording of previous events Records Reporting Sampling Screening procedure Semantics Sensitivity and Specificity Shortness of Breath Signal Transduction Smallpox Structure Symptoms System Techniques Temperature Text Translating Triage Variant acronyms base experience improved lexical mortality pandemic disease population based prototype research to practice response satisfaction syntax tool

项目摘要

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.

描述（由申请人提供）：早期发现疾病暴发可以降低患者的发病率和死亡率，并最大程度地减少疾病的传播。早期检测需要在患者疾病的早期对患者症状进行准确分类。一种方法是生物监视，其中在疾病的早期捕获了电子症状数据，并分析了可能表明需要进行公共卫生系统调查和反应的爆发的信号。鉴于其及时的电子可用性，急诊科（ED）患者记录对生物监视特别有用。监视系统中使用的ED数据元素包括主要投诉（对患者的主要症状的简要说明）和分类护士的注释（也称为当前疾病的史）。主要的投诉是最广泛使用的ED数据元素，因为大多数EDS都以电子方式记录了ED数据元素。一项研究表明，添加分类音符提高了生物监视病例检测的灵敏度。提高敏感性是因为分类音符增加了可用的数据量：而不是主要投诉中的一种症状（例如发烧），而是分类笔记可能包含多种症状（例如，“发烧，咳嗽和呼吸暂停12个小时”）。但是，通过ED首席投诉和分类说明中免费文本数据的广泛差异，监视工作受到了阻碍。它们通常包括拼写错误，缩写，首字母缩写词以及其他难以分组为症状簇（例如，发烧，温度104，FVR，FEBRILE）的词汇和语义变体。需要工具来解决ED数据中症状术语的词汇和语义变化，以改善生物监视。自然语言处理工具已被证明可以促进从更结构化的临床数据（例如放射学报告）中提取概念，但是这些技术的应用有限地应用于免费文本ED Triage注释。项目团队开发了紧急医疗文本处理器（EMT-P），以预处理主要投诉。 EMT-P可以清理并归一化简短的主要投诉条目，然后提取标准化的概念，但目前的状态不足以预处理更长，更复杂的文本段落，例如Triage Notes。该提议的项目将通过调整EMT-P和其他统计和经典的自然语言处理工具来进一步增强生物监视，以开发一种从Triage Notes中提取概念的系统。

项目成果

期刊论文数量（1）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Implementation of Emergency Medical Text Classifier for syndromic surveillance.

实施用于症状监测的紧急医疗文本分类器。

DOI：
发表时间：
2013
期刊：
AMIA ... Annual Symposium proceedings. AMIA Symposium
影响因子：
0
作者：
Travers,Debbie;Haas,StephanieW;Waller,AnnaE;Schwartz,ToddA;Mostafa,Javed;Best,NakiaC;Crouch,John
通讯作者：
Crouch,John