Joint learning methods for event and relation extraction from clinical narratives

项目摘要 电子健康记录（EHR）详细说明了患者状态和临床护理的各个方面， 质量改进和监督举措以及革新临床研究。非结构化 电子病历中的临床叙述记录了重要信息，包括医疗问题、治疗方法和 诊断测试以及护理和结果的基本原理。自然语言处理（NLP） 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。 这些系统提取诸如医疗问题、治疗和测试的临床概念，确定治疗方案。 这些概念的属性，以明确其在患者中的存在/不存在和其他细节;并识别 这些概念在预定义的关系方面相互作用。大多数临床NLP系统 处理该信息的提取的技术是基于流水线的：即，临床概念的提取先于 确定它们的属性和确定临床概念之间的关系。同时产生 这些系统有两个主要的局限性：（1）当面对数据不平衡时， 在数据中发现的更普遍的观测类别上表现最好，而在不太普遍的观测类别上表现最差。 1，以及（2）它们允许错误在组件之间级联。这两个限制也可以 互相配合。因此，NLP系统提取的信息可能是不完整和粗糙的- 颗粒化，无法支持需要更细粒度的患者状况图片的临床应用。 在这个项目中，我们建议解决这些限制的临床信息提取任务，旨在 用一种新颖的、细粒度的、层次化的模式来捕捉患者状况的更完整的画面， 临床显著事件及其关系。我们将临床显著事件定义为医学问题， 治疗，以及在患者护理过程中记录的测试。我们在一帧中捕捉每个事件， 一个触发器和一组细粒度的属性。我们在事件之上建立事件-事件关系。解决 数据不平衡，我们提出（i）一个新的主动学习框架，指导手动注释工作 这些样本可以促进对不太普遍的属性的自动识别， 关系为了解决级联错误，我们提出了（ii）一个新的联合学习系统，使多个任务 相互通知，以便在所有任务中获得更好的性能。我们在多种音符类型上评估我们的工作 来自多个机构。预期成果包括：（1）全面的异质金本位制 从多个机构为临床显著事件和关系创建的数据集，（2）NLP方法， 在提取事件和关系方面产生最先进的结果，以及（3）记录我们的 调查结果。注释指南和模式、黄金标准注释以及NLP模型和工具 项目期间创建的数据将与研究社区共享。

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.