NLP Foundational Studies & Ontologies for Syndromic Surveillance from ED Reports

描述(由申请人提供)： 已经成功地开发了许多NLP应用程序来从文本中提取信息。大多数 应用程序侧重于识别文本记录中的个别临床情况，这是使这些条件可用于计算机化应用程序的第一步。然而，对于许多医学信息学任务来说，识别临床条件的个别实例是不够的--围绕条件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NLP问题进行深入研究，这些问题需要了解临床记录中的临床情况。我们将使用急诊科(ED)报告中的症状监测作为个案研究，以此为研究重点。 对于这一建议，我们将检验以下假设：一个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急诊报告中识别急性临床情况。 我们将确定监测七种综合征所需的临床概念，包括呼吸系统、 胃肠道、神经系统、皮疹、出血性、体质和肉毒杆菌。为了评估这一假设，我们将执行以下具体目标： 目标1.对四个NLP主题进行深入的基础性研究，以加深对 从急诊报告中识别急性临床情况所需的相关NLP研究能力，包括否定、不确定性、时间歧视和发现验证； 目的2.应用从基础研究中学到的知识来开发和评估ED报告的自动应用程序，该应用程序将确定与识别具有七种综合征中的任何一种症状的患者相关的临床变量的值。 这项研究具有创新性，因为它将产生对多个NLP主题的深入研究，这些主题对 从文字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。这项研究将以语言学原则为指导，以ED报告的语义和语篇结构为指导，以生物监测的应用领域为指导。由于我们将开发针对特定领域定制的研究方法和工具，我们将限制研究空间，这将提供方向并提高成功的机会。然而，本研究生成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的应用程序，因为我们将明确指定和研究许多应用领域共同的NLP概念和关系。

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.