Natural Language Processing for Respiratory Surveillance

用于呼吸监测的自然语言处理

• 批准号: 6898458
• 负责人: WENDY W. CHAPMAN
• 金额: $ 16.2万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-07-01 至 2007-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6898458
• 关键词: automated data processing; automated medical record system; bioinformatics; bioterrorism /chemical warfare; clinical research; communicable disease diagnosis; computer assisted diagnosis; computer system design /evaluation; diagnosis design /evaluation; disease outbreaks; early diagnosis; hospital utilization; human data; language translation; respiratory disorder epidemiology; severe acute respiratory syndrome; sign /symptom

DESCRIPTION (provided by applicant): The applicant's long-term career goal is to become an independent investigator in biomedical informatics research. She will dedicate her career to developing and evaluating methodologies for improving the processes and outcomes of healthcare using data locked in textual documents. This career development award will provide her with initial support for achieving her career goals. The applicant has three goals for her career development over the next three years. First, she will compare the performance of different machine learning techniques at detecting patients with a respiratory syndrome. The proposed research will expand the state-of-the-art syndromic surveillance capabilities by integrating findings, symptoms, and diseases described in textual medical records. The product of the first research goal will be a model for respiratory syndromic case detection for monitoring natural and bioterrorism induced respiratory outbreaks. Second, she will apply existing methods and develop new techniques for extracting clinical conditions required for respiratory case detection from emergency department notes, contributing new knowledge to the medical language processing field using sentence and report level models that account for uncertainty, negation, and temporal occurrence. The product of the second goal will be a better understanding of the information required for accurate detection of respiratory related conditions from text and useful tools for automatically extracting that information. Third, she will teach, promote, and facilitate the use of natural language processing in the biomedical informatics field. The product of the third goal will be a graduate class surveying medical language processing methodology and applications and development of general tools sets for researchers who need encoded data from textual patient records. The proposed research will focus on: Aim 1. Development and evaluation of a respiratory case detection model; Aim 2. Integration of existing natural language processing tools and development of new methodologies for extracting clinical conditions needed for respiratory case detection from textual records; and Aim 3. Comparison of existing syndromic detection algorithms that use admit data against the same algorithms using conditions extracted from textual reports.

描述（由申请人提供）：申请人的长期职业目标是成为生物医学信息学研究的独立研究者。她将专注于开发和评估方法，以使用锁定文本文档中的数据来改善医疗保健的过程和结果。这个职业发展奖将为她提供最初的支持，以实现自己的职业目标。 申请人在未来三年内实现了她的职业发展三个目标。首先，她将比较不同机器学习技术的性能，以检测患有呼吸系统综合征的患者。拟议的研究将通过整合文本医疗记录中描述的发现，症状和疾病来扩大最新的综合症监测能力。第一个研究目标的产物将是用于监测自然和生物恐怖诱导呼吸道暴发的呼吸综合症病例检测模型。其次，她将采用现有方法并开发新技术来从急诊室票据中提取呼吸病例检测所需的临床状况，并使用句子和报告级别模型为医学语言处理领域提供新知识，以解释不确定性，否定和时间的发生。第二个目标的产物将更好地理解从文本和有用工具中准确检测相关条件所需的信息，以自动提取该信息。第三，她将在生物医学信息学领域教授，促进和促进使用自然语言处理。第三个目标的产物将是研究生类调查医学语言处理方法和应用程序，并为需要从文本患者记录中编码数据的研究人员开发一般工具集。 拟议的研究将重点关注： 目标1。呼吸案例检测模型的开发和评估； 目标2。现有的自然语言处理工具的整合和开发新方法，用于从文本记录中提取呼吸病例检测所需的临床条件；和 AIM 3。使用从文本报告中提取的条件对相同算法使用录取数据的现有综合检测算法进行比较。