Natural Language Processing for Respiratory Surveillance

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调查员。她将致力于开发和评估使用锁定在文本文档中的数据来改善医疗保健流程和结果的方法。这个职业发展奖将为她实现职业目标提供初步支持。 申请人在未来三年的职业发展中有三个目标。首先，她将比较不同机器学习技术在检测呼吸综合征患者方面的性能。拟议的研究将通过整合文本医疗记录中描述的发现，症状和疾病来扩展最先进的综合征监测能力。第一个研究目标的产品将是一个呼吸综合征病例检测模型，用于监测自然和生物恐怖主义引起的呼吸道疾病爆发。其次，她将应用现有的方法，并开发新的技术，从急诊科笔记中提取呼吸病例检测所需的临床条件，使用句子和报告级别模型为医学语言处理领域提供新的知识，这些模型考虑了不确定性，否定和时间发生。第二个目标的产物将是更好地理解从文本中准确检测呼吸相关状况所需的信息以及用于自动提取该信息的有用工具。第三，她将教授，促进和促进自然语言处理在生物医学信息学领域的使用。第三个目标的产品将是一个研究生班，调查医学语言处理方法和应用程序，并为需要文本病历编码数据的研究人员开发通用工具集。 拟议的研究将侧重于： 目标1。呼吸道病例检测模型的发展和评价 目标二。整合现有的自然语言处理工具，并开发新的方法，从文本记录中提取呼吸病例检测所需的临床条件; 目标3。使用入院数据的现有综合征检测算法与使用从文本报告中提取的条件的相同算法的比较。

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.