NLP Foundational Studies & Ontologies for Syndromic Surveillance from ED Reports

NLP基础研究

• 批准号: 7908086
• 负责人: WENDY W. CHAPMAN
• 金额: $ 13.09万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2010-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7908086
• 关键词: Accident and Emergency department; Acute; Area; Case Study; Clinical; Clinical Data; Constitutional; Detection; Discrimination; Exanthema; Exhibits; Individual; Informatics; Knowledge; Learning; Linguistics; Medical Informatics; Medical Informatics Applications; Methods; Natural Language Processing; Neurologic; Ontology; Patients; Physicians; Records; Reporting; Research; Research Methodology; Semantics; Specific qualifier value; Structure; Syndrome; System; Techniques; Testing; Text; Uncertainty; Validation; computerized; gastrointestinal; indexing; innovation; quality assurance; respiratory; success; syndromic surveillance; tool

DESCRIPTION (provided by applicant): Many NLP applications have been successfully developed to extract information from text. Most of the applications have focused on identifying individual clinical conditions in textual records, which is the first step in making the conditions available to computerized applications. However, identifying individual instances of clinical conditions is not sufficient for many medical informatics tasks - the context surrounding the condition is crucial for integrating the information within the text to determine the clinical state of a patient. We propose to perform in-depth studies on NLP issues requiring knowledge of the context of clinical conditions in clinical records. We will focus our research by using syndromic surveillance from emergency department (ED) reports as a case study. For this proposal, we will test the following hypothesis: An NLP system that indexes clinical concepts and integrates contextual information modifying the concepts can identify acute clinical conditions from ED reports as well as physicians can. We will identify clinical concepts necessary for surveillance of seven syndromes, including respiratory, gastrointestinal, neurological, rash, hemorrhagic, constitutional, and botulinic. To evaluate the hypothesis, we will perform the following specific aims: Aim 1. Perform in-depth, foundational studies on four NLP topics to gain a deeper understanding of the pertinent NLP research capabilities required for identification of acute clinical conditions from ED reports, including negation, uncertainty, temporal discrimination, and finding validation; Aim 2. Apply the knowledge learned from the foundational studies to develop and evaluate an automated application for ED reports that will determine the values for clinical variables relevant to identifying patients with any of seven syndromes. The research is innovative, because it will generate an in-depth study of multiple NLP topics crucial to understanding a patient's clinical state from textual records and will focus on contextual understanding and analysis. The research will be guided by linguistic principles, by the semantics and discourse structure of ED reports, and by the application area of biosurveillance. Because we will develop research methods and tools that are customized to a particular domain, we will constrain the research space, which will provide direction and enhance the chance for success. However, the methods and tools generated by this research should be extensible to other clinical report types and to other domain applications, because we will explicitly specify and study NLP concepts and relationships that are common to many application areas.

描述（由申请人提供）： 许多 NLP 应用程序已成功开发用于从文本中提取信息。大部分的 应用程序的重点是识别文本记录中的个体临床状况，这是使这些状况可供计算机应用程序使用的第一步。然而，识别临床状况的个体实例对于许多医疗信息学任务来说是不够的——围绕该状况的上下文对于将信息整合到文本中以确定患者的临床状态至关重要。我们建议对需要了解临床记录中临床状况背景的 NLP 问题进行深入研究。我们将通过使用急诊科 (ED) 报告中的症状监测作为案例研究来集中研究。 对于该提案，我们将测试以下假设：索引临床概念并整合修改概念的上下文信息的 NLP 系统可以像医生一样从 ED 报告中识别急性临床状况。 我们将确定监测七种综合征所需的临床概念，包括呼吸道、 胃肠道、神经、皮疹、出血、体质和肉毒杆菌。为了评估该假设，我们将实现以下具体目标： 目标 1. 对四个 NLP 主题进行深入的基础研究，以更深入地了解 从 ED 报告中识别急性临床状况所需的相关 NLP 研究能力，包括否定、不确定性、时间歧视和结果验证； 目标 2. 应用从基础研究中学到的知识来开发和评估 ED 报告的自动化应用程序，该应用程序将确定与识别七种综合症患者相关的临床变量的值。 这项研究具有创新性，因为它将对多个 NLP 主题进行深入研究，这对 从文本记录中了解患者的临床状态，并将重点放在上下文理解和分析上。该研究将以语言学原理、ED报告的语义和话语结构以及生物监测的应用领域为指导。因为我们将开发针对特定领域定制的研究方法和工具，所以我们将限制研究空间，这将提供方向并增加成功的机会。然而，这项研究产生的方法和工具应该可以扩展到其他临床报告类型和其他领域应用，因为我们将明确指定和研究许多应用领域常见的 NLP 概念和关系。