A VHA NLP Software Ecosystem for Collaborative Development and Integration

用于协作开发和集成的 VHA NLP 软件生态系统

• 批准号: 8488627
• 负责人: QING ZENG
• 金额: --
• 依托单位: VA SALT LAKE CITY HEALTHCARE SYSTEM
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-12-01 至 2017-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8488627
• 关键词: Administrator; Adoption; Biology; Characteristics; Clinical; Collaborations; Collection; Communities; Computer software; Computerized Medical Record; Consensus; Data; Data Set; Development; Ecosystem; Environment; Fostering; Funding; Genomics; Goals; Health; Health Services; Health Services Research; Individual; Informatics; Label; Medical; Medical Records; Methods; Modeling; Natural Language Processing; Patient Care; Patients; Play; Process; Production; Public Health Informatics; Records; Research; Research Infrastructure; Research Personnel; Research Project Grants; Role; Science; Semantics; Solutions; System; Text; base; biomedical resource; data modeling; design; information processing; interoperability; meetings; prototype; research and development; tool

DESCRIPTION (provided by applicant): The VA has invested hugely in electronic medical records and has achieved a nationwide system that collects medical information from all patients. Currently, the textual information in the medical records is inaccessible to all but a small number of researchers. In order to obtain the highest value from this existing system, administrators and practitioners need to be able to access the textual information they need. It is our responsibility to get the most benefit from thi resource for biomedical and patient care. Clinical natural language processing (NLP) is an important part the solution. The value of NLP has been recognized in the biomedical domain. Evidence of this includes funding for the following national initiatives focused on clinical NLP: Integrating Biology and the Bedside (i2b2), Consortium for Health Informatics Research (CHIR), VA Informatics and Computing Infrastructure (VINCI), Strategic Health IT Advanced Research Projects (SHARP), and electronic Medical Records & Genomics (eMERGE). On the one hand, these efforts testify to the demand for NLP research. They have produced new NLP tools, created annotated datasets, developed common data models, shared semantic labels, and even piloted a prototype software ecosystem. On the other hand, the general consensus in the informatics community is that processing and utilizing textual data remains challenging due to lack of interoperability and collaboration. Unless the pace of research and development is accelerated in clinical NLP, we cannot meet the increasing NLP demand originated from the biomedical and health services research community. Although synergistic development has the promise of advancing the science of NLP and accelerating the pace of NLP tool production, there lacks a vibrant collaborative environment attracting participation of a significant number of clinical NLP developers and researchers. Within the VA CHIR and VINCI efforts, we have created a prototype NLP ecosystem called V3NLP that supports the interoperability and integration of heterogeneous tools into VA research and operational initiatives. The environment needed to foster collaboration and a critical mass of users, however, is lacking. In the proposed project, we will study the needs of existing and potential users of the V3NLP ecosystem to increase its utility and ease of adoption and to facilitate collaboration. The ultimate goal of an NLP ecosystem is to produce new and more accurate NLP methods for clinical text. This requires a good understanding of the characteristics of various types of clinical text and the strengths and weakness of existing methods. Because most clinical NLP solutions have been driven by individual use cases and note collections, the resultant solutions are optimized for the characteristics of the specific NLP tasks and text corpora analyzed. Since there are numerous tasks and corpora, clinical NLP solutions tend to be difficult to re-use, especially by different developers. To remedy this, we will research characteristics of a very large and heterogeneous collection of VA text records to understand and model sublanguages in VA clinical notes. This systematic and comprehensive sublanguage analysis will play a critical role in the proposed ecosystem. It will guide the development of new clinical NLP methods as well as the customization of existing solutions. Our general goal is to accelerate clinical NLP research and development. The specific aims are as follows: (1) Collect and analyze the needs of NLP developers, health informatics researchers and health services researchers to inform the design of a collaborative NLP ecosystem that will facilitate development of more accurate methods. (2) Design and implement a clinical NLP ecosystem that fosters collaboration and accelerates research and adoption of accurate and generalizable NLP methods. (3) Conduct a comprehensive sublanguage analysis to guide the creation of adaptable NLP tools and methods based on VA text notes to support text processing and information extraction across multiple clinical domains.

描述（由申请人提供）： 退伍军人管理局在电子病历方面投入巨资，并建立了一个全国性的系统，可以收集所有患者的医疗信息。目前，除了少数研究人员之外，所有人都无法获取病历中的文本信息。为了从现有系统中获得最高价值，管理员和从业人员需要能够访问他们所需的文本信息。我们有责任从生物医学和患者护理资源中获得最大利益。临床自然语言处理（NLP）是该解决方案的重要组成部分。 NLP的价值已在生物医学领域得到认可。这方面的证据包括为以下专注于临床 NLP 的国家计划提供资金：整合生物学和床边 (i2b2)、健康信息学研究联盟 (CHIR)、VA 信息学和计算基础设施 (VINCI)、战略健康 IT 高级研究项目 (SHARP) ）以及电子病历和基因组学 (eMERGE)。一方面，这些努力证明了对 NLP 研究的需求。他们开发了新的 NLP 工具、创建了带注释的数据集、开发了通用数据模型、共享语义标签，甚至还试点了原型软件生态系统。另一方面，信息学界的普遍共识是，由于缺乏互操作性和协作，处理和利用文本数据仍然具有挑战性。除非加快临床NLP的研发步伐，否则我们无法满足来自生物医学和健康服务研究界日益增长的NLP需求。 尽管协同发展有望推进 NLP 科学并加快 NLP 工具生产的步伐，但缺乏一个吸引大量参与者参与的充满活力的协作环境 临床 NLP 开发人员和研究人员。在 VA CHIR 和 VINCI 的努力下，我们创建了一个名为 V3NLP 的原型 NLP 生态系统，支持异构工具的互操作性和集成到 VA 研究和运营计划中。然而，缺乏促进协作和足够数量的用户所需的环境。在拟议的项目中，我们将研究 V3NLP 生态系统现有和潜在用户的需求，以提高其实用性和易用性并促进协作。 的最终目标是 NLP生态系统旨在为临床文本产生新的、更准确的NLP方法。 这需要充分了解各类临床文本的特点以及现有方法的优缺点。由于大多数临床 NLP 解决方案都是由单个用例和笔记集合驱动的，因此最终的解决方案针对特定 NLP 任务和分析的文本语料库的特征进行了优化。由于任务和语料库众多，临床 NLP 解决方案往往难以重复使用，尤其是不同的开发人员。为了解决这个问题，我们将研究非常大且异构的 VA 文本记录集合的特征，以理解和建模 VA 临床记录中的子语言。这种系统且全面的子语言分析将在拟议的生态系统中发挥关键作用。它将指导新的临床 NLP 方法的开发以及现有解决方案的定制。 我们的总体目标是加速临床 NLP 的研究和开发。具体目标如下：（1）收集和分析 NLP 开发人员、健康信息学研究人员和健康服务研究人员的需求，为设计协作 NLP 生态系统提供信息，从而促进更准确方法的开发。 (2) 设计和实施一个临床 NLP 生态系统，促进协作并加速准确和可推广的 NLP 方法的研究和采用。 (3) 进行全面的子语言分析，指导基于 VA 文本注释创建适应性强的 NLP 工具和方法，以支持跨多个临床领域的文本处理和信息提取。