Joint learning methods for event and relation extraction from clinical narratives

从临床叙述中提取事件和关系的联合学习方法

基本信息

批准号：
10507223
负责人：
Ozlem Uzuner
金额：
$ 42.49万
依托单位：
GEORGE MASON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-10 至 2025-07-31
项目状态：
未结题

项目摘要

Project Summary Electronic health records (EHRs), detailing patient status and all aspects of clinical care, can greatly facilitate quality improvement and surveillance initiatives as well as revolutionize clinical research. The unstructured clinical narratives in EHRs document critical information, including medical problems, treatments, and diagnostic tests as well as the rationale for care and outcomes. Natural Language Processing (NLP) and Information Extraction (IE) systems target the identification of such critical information from clinical narratives. These systems extract clinical concepts such as medical problems, treatments, and tests, determine the attributes of these concepts to get clarity on their presence/absence and other details in a patient; and identify the interactions of these concepts with each other in terms of predefined relations. Most clinical NLP systems that tackle the extraction of this information are pipeline based: i.e., extraction of clinical concepts precedes the determination of their attributes and the determination of relations between clinical concepts. While producing promising results, these systems suffer from two major limitations: (1) when faced with data imbalance, they perform best on the more prevalent classes of observations found in the data and suffer on the less prevalent ones, and (2) they allow errors to cascade between the components. These two limitations can also compound each other. As a result, the information extracted by NLP systems can be incomplete and coarse- grained, unable to support clinical applications that require a more fine-grained picture of the patient condition. In this project, we propose to address these limitations on a clinical information extraction task that aims to capture a more complete picture of the patient condition with a novel, fine-grained, hierarchical schema for clinically-salient events and their relations. We define clinically-salient events as medical problems, treatments, and tests that are documented during patient care. We capture each event in a frame that consists of a trigger and a set of fine-grained attributes. We build event–event relations on top of events. To address data imbalance, we propose (i) a novel active learning framework that guides manual annotation efforts towards diverse and informative samples that can boost automated recognition of less prevalent attributes and relations. To address cascading errors, we propose (ii) a novel joint learning system that enables multiple tasks to inform each other for better performance across all tasks. We evaluate our work on multiple note types from multiple institutions. Expected outcomes include (1) a comprehensive heterogeneous gold-standard dataset created from multiple institutions for clinically-salient events and relations, (2) NLP methods that generate state-of-the-art results in extraction of events and relations, and (3) publications that document our findings. The annotation guidelines and schema, the gold-standard annotations, and the NLP models and tools created during the project will be shared with the research community.

项目摘要电子健康记录（EHR），详细介绍患者状况和临床护理的各个方面，可以极大地促进质量改进和监视计划以及彻底改变临床研究。非结构化 EHRS中的临床叙述记录了关键信息，包括医疗问题，治疗和诊断测试以及护理和结果的基本原理。自然语言处理（NLP）和信息提取（IE）系统的目标是从临床叙述中识别此类关键信息。这些系统提取临床概念，例如医疗问题，治疗和测试，确定这些概念的属性是为了清楚其存在/不存在以及患者的其他细节；并确定这些概念在预定义的关系方面彼此之间的相互作用。大多数临床NLP系统解决此信息的提取是基于管道的：即，提取临床概念之前确定其属性和临床概念之间关系的确定。同时生产有希望的结果，这些系统受到了两个主要局限性：（1）面临数据不平衡时，它们在数据中发现的较普遍的观察结果中表现最好其中一个，（2）它们允许在组件之间级联的错误。这两个限制也可以互相复合。结果，NLP系统提取的信息可能不完整，粗糙粒状，无法支持需要对患者病情进行更细粒度的临床应用。在这个项目中，我们建议解决针对临床信息提取任务的这些限制，旨在使用新颖的，细粒度的分层模式来捕获患者状况的更完整的情况临床上的事件及其关系。我们将临床上的事件定义为医疗问题，治疗和在患者护理过程中记录的测试。我们在一个框架中捕获每个事件触发器和一组细粒属性。我们在事件之外建立事件 - 事件关系。解决数据失衡，我们提出了一个新型的主动学习框架，以指导手动注释工作迈向潜水和信息丰富的样本，这些样本可以提高对较少普遍属性的自动识别和关系。为了解决级联错误，我们建议（ii）一个新型的联合学习系统，可以实现多个任务互相告知对所有任务的表现更好。我们在多种音符类型上评估我们的工作来自多个机构。预期结果包括（1）全面的异质金标准数据集由多个机构创建的用于临床上的事件和关系，（2）NLP方法生成最先进的结果会导致事件和关系的提取，以及（3）证明我们的发现。注释指南和架构，金标准注释以及NLP模型和工具在项目期间创建的将与研究社区共享。