Semi-structured Information Retrieval in Clinical Text for Cohort Identification

项目摘要 电子健康记录(EHR)的广泛采用使临床数据能够用于临床 研究和医疗保健服务。许多机构已经建立了临床数据仓库(CDW) 与队列发现工具(例如i2b2)结合使用，以支持临床研究使用临床数据 包括回溯性临床研究以及可行性评估或临床试验的患者招募。 然而，很大一部分相关的患者信息嵌入到临床叙述中，并自然地 在使用电子病历数据时，信息提取等语言处理(NLP)技术至关重要 临床研究。已经开发了许多临床NLP系统来从文本中提取信息以用于各种 下游应用程序，但存在性能和可移植性问题。信息检索 (IR)，搜索引擎中使用的一种技术，用于存储、检索和排序来自 基于用户查询的文本文档可以提供另一种利用临床叙述的方法 队列发现，因为它对语义的依赖较少。要做到这一点，还需要额外的工作。 由于当前的信息检索方法通常是基于文档的，并且将队列发现作为信息检索的表述 任务要求开发创新的IR方法来处理复杂的EHR数据和队列标准 上下文(例如，空间或时间)约束。 我们的长期目标是开发信息学解决方案，以加快临床研究中电子病历数据的使用。 这项建议的主要目标是开发创新的IR方法，这些方法从EHR中形成队列发现 将数据作为一项IR任务，旨在加快确定用于队列研究或招募的患者队列 符合临床试验条件的患者名单。在我们目前的R01支持的研究(R01LM011934)中，我们引入了新的 支持重用NLP产生的人工产物用于基于IR的队列检索的语言模型，并开发了 两个机构(梅奥诊所和OHSU)的IR评估的平行资源。我们假设，给定 具有上下文约束的复杂队列标准、具有定制的体系结构组件的IR框架(例如， 索引、排名、评估和查询处理)用于存储和查询EHR数据 用于查询非结构化电子病历数据的传统队列发现工具以及基于文本的优势 用于查询结构化和非结构化EHR数据的搜索引擎。至于建议的重建计划，我们计划 I)采用通用数据模型(CDM)并在另一个地点部署该框架，以评估 方法的概括性，二)扩展信息检索框架，以纳入上下文信息，三) 将深层语义表示纳入信息检索框架。如果成功，拟议的项目将 在队列发现和识别方面的高级信息学研究，基于以下方面影响了许多应用 EHR数据，如学习医疗保健系统、预测建模或医疗保健中的人工智能。

Project Summary The widespread adoption of Electronic Health Records (EHRs) has enabled the use of clinical data for clinical research and healthcare delivery. Many institutions have established clinical data warehouses (CDWs) in conjunction with cohort discovery tools (e.g., i2b2) to support the use of clinical data for clinical research including retrospective clinical studies as well as feasibility assessment or patient recruitment for clinical trials. However, a significant portion of relevant patient information is embedded in clinical narratives and natural language processing (NLP) techniques such as information extraction are critical when using EHR data for clinical research. Many clinical NLP systems have been developed to extract information from text for various downstream applications but have had unsatisfactory performance and portability issues. Information retrieval (IR), a technique used in search engines for storing, retrieving, and ranking documents from a large collection of text documents based on users’ queries, can provide an alternative approach to leverage clinical narratives for cohort discovery as it is less dependent on semantics. In order to accomplish this, additional work is needed since current IR approaches are generally document-based and the formulation of cohort discovery as an IR task requires the development of innovative IR approaches to handle complex EHR data and cohort criteria with contextual (e.g., spatial or temporal) constraints. Our long-term goal is to develop informatics solutions to accelerate the use of EHR data for clinical research. The main goal of this proposal is to develop innovative IR methods, which formulate cohort discovery from EHR data as an IR task, aiming to accelerate the identification of patient cohorts for cohort studies or the recruitment of eligible patients for clinical trials. In our current R01-supported study (R01LM011934), we introduced novel language models to enable the reuse of NLP-produced artifacts for IR-based cohort retrieval and developed parallel resources for IR evaluation at two institutions (Mayo Clinic and OHSU). We hypothesize that, given complex cohort criteria with contextual constraints, an IR framework with tailored architecture components (e.g., indexing, ranking, evaluation, and query processing) for storing and querying EHR data has an advantage over traditional cohort discovery tools for querying unstructured EHR data as well as an advantage over text-based search engines for querying both structured and unstructured EHR data. For the proposed renewal, we plan to i) adopt common data models (CDMs) and deploy the framework at one additional site to assess the generalizability of methods, ii) extend the IR framework to incorporate contextual information, and iii) incorporate deep semantic representations into the IR framework. If successful, the proposed project will advance informatics research on cohort discovery and identification, which impacts many applications based on EHR data such as learning healthcare systems, predictive modeling, or AI in healthcare.