NLP Foundational Studies & Ontologies for Syndromic Surveillance from ED Reports

描述（由申请人提供）： 许多NLP应用程序已经成功地开发出从文本中提取信息。大部分 应用集中于在文本记录中识别个体临床状况，这是使状况可用于计算机化应用的第一步。然而，识别临床状况的单个实例对于许多医学信息学任务是不够的-围绕状况的上下文对于整合文本中的信息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状况。我们将集中我们的研究，使用症状监测从急诊科（艾德）报告作为一个案例研究。 对于这个建议，我们将测试以下假设：一个NLP系统，索引临床概念，并整合上下文信息修改的概念，可以识别急性临床条件从艾德报告，以及医生可以。 我们将确定监测七种综合征所需的临床概念，包括呼吸道综合征， 胃肠、神经、皮疹、出血、体质和肉毒杆菌。为了评估假设，我们将执行以下特定目标： 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 从艾德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、时间辨别和发现验证; 目标2.应用从基础研究中学到的知识，开发和评估艾德报告的自动化应用程序，该应用程序将确定与识别具有七种综合征中任何一种的患者相关的临床变量的值。 这项研究是创新的，因为它将产生对多个NLP主题的深入研究，这些主题对 从文本记录中了解患者的临床状态，并将侧重于上下文理解和分析。本研究将以语言学原理、艾德报告的语义和语篇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的研究方法和工具，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，本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的应用程序，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。

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.