Semi-structured Information Retrieval in Clinical Text for Cohort Identification

项目摘要 电子健康记录（EHR）的广泛采用使得能够将临床数据用于临床诊断。 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）， 结合群组发现工具（例如，i2b2）支持使用临床数据进行临床研究 包括回顾性临床研究以及可行性评估或临床试验的患者招募。 然而，相关患者信息的很大一部分嵌入在临床叙述中， 语言处理（NLP）技术，如信息提取，在使用EHR数据进行 临床研究已经开发了许多临床NLP系统，以从文本中提取各种信息。 下游应用程序，但具有不令人满意的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，用于从大量的 基于用户查询的文本文档，可以提供一种替代方法来利用临床叙述， 队列发现，因为它较少依赖于语义。为了做到这一点，需要做更多的工作 由于当前的IR方法通常是基于文档的，并且将群组发现公式化为IR 这项任务需要开发创新的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，以加速EHR数据在临床研究中的使用。 该提案的主要目标是开发创新的IR方法，该方法从EHR制定队列发现 数据作为IR任务，旨在加速队列研究的患者队列识别或招募 临床试验的合格患者。在我们目前的R01支持的研究（R01LM011934）中，我们引入了新的 语言模型，使重用NLP产生的文物为基础的IR队列检索和开发 在两个机构（马约诊所和OHSU）进行IR评价的平行资源。我们假设， 具有上下文约束的复杂群组标准，具有定制架构组件的IR框架（例如， 索引、排名、评估和查询处理）的优势， 用于查询非结构化EHR数据的传统队列发现工具， 用于查询结构化和非结构化EHR数据的搜索引擎。对于拟议的更新，我们计划 i）采用通用数据模型，并在另一个地点部署框架，以评估 方法的可推广性，ii）扩展IR框架以纳入上下文信息，以及iii） 将深层语义表示纳入IR框架。如果成功，该项目将 推进队列发现和识别的信息学研究，这影响了许多基于 EHR数据，如学习医疗保健系统，预测建模或医疗保健中的AI。

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.