Semi-structured Information Retrieval in Clinical Text for Cohort Identification

用于队列识别的临床文本中的半结构化信息检索

基本信息

批准号：
10879792
负责人：
WILLIAM R HERSH
金额：
$ 63.75万
依托单位：
UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-09-20 至 2026-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10879792
关键词：
Acceleration Adopted Adoption Algorithms Architecture COVID-19 Clinic Clinical Clinical Data Clinical Research Clinical Trials Cohort Studies Collection Communities Complex Data Development Electronic Health Record Eligibility Determination Evaluation Feedback Formulation Goals Healthcare Healthcare Systems Human Informatics Information Retrieval Institution Language Learning Medical Methods Modeling Morphologic artifacts Natural Language Processing Outcome Patient Recruitments Patients Performance Pharmaceutical Preparations Process Research Resources Retrieval Semantics Site Structure System Techniques Text Time Training Translational Research Visit Work clinical data warehouse clinical research site clinical trial recruitment cohort data modeling data standards density design experimental study health care delivery heterogenous data indexing innovation learning strategy neural novel open source portability predictive modeling query tools recruit search engine structured data tool

项目摘要

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.

项目摘要电子健康记录的宽度采用（EHR）已使临床数据用于临床研究和医疗保健提供。许多机构已经建立了临床数据仓库（CDW）与队列发现工具的联系（例如，I2B2）支持使用临床数据的临床研究包括回顾性临床研究以及可行性评估或患者招募临床试验。但是，相关患者信息的很大一部分嵌入到临床叙述和自然中使用EHR数据时，语言处理（NLP）技术（例如信息提取）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下游应用程序，但性能和可移植性问题不令人满意。信息检索（ir），一种用于搜索引擎中用于存储，检索和排名文档的技术基于用户查询的文本文档可以提供另一种方法来利用临床叙述队列发现，因为它不太依赖语义。为了实现这一目标，需要其他工作由于当前的IR方法通常是基于文档的，并且同类发现的公式作为IR 任务需要开发创新的IR方法，以处理复杂的EHR数据和队列标准上下文（例如，空间或临时）约束。我们的长期目标是开发信息解决方案，以加速使用EHR数据进行临床研究。该提案的主要目标是开发创新的IR方法，该方法从EHR中提出了同类发现数据作为IR任务，旨在加速对队列研究或招聘的患者队列的识别合格的患者进行临床试验。在当前的R01支持研究（R01LM011934）中，我们引入了新颖语言模型能够重新使用基于IR的同类群体检索NLP生产的工件在两个机构（Mayo Clinic和OHSU）进行IR评估的并行资源。我们假设这是给定的具有上下文约束的复杂队列标准，这是一个带有量身定制的体系结构组件的IR框架（例如，存储和查询EHR数据的索引，排名，评估和查询处理）优于传统的队列发现工具，用于查询非结构化EHR数据以及优于基于文本的优势搜索引擎查询结构化和非结构化EHR数据。对于拟议的续约，我们计划 i）采用常见数据模型（CDM）并将框架部署在另一个站点中以评估方法的概括性，ii）扩展IR框架以合并上下文信息，以及III）将深层语义表示形式纳入IR框架。如果成功，则建议的项目将有关队列发现和身份证明的提前信息研究，这影响了许多基于 EHR数据，例如学习医疗系统，预测性建模或医疗保健中的AI。

项目成果

期刊论文数量（34）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

MedTator: a serverless annotation tool for corpus development.

MedTator：用于语料库开发的无服务器注释工具。

DOI：
10.1093/bioinformatics/btab880
发表时间：
2022
期刊：
Bioinformatics (Oxford, England)
影响因子：
0
作者：
He,Huan;Fu,Sunyang;Wang,Liwei;Liu,Sijia;Wen,Andrew;Liu,Hongfang
通讯作者：
Liu,Hongfang

The IMPACT framework and implementation for accessible in silico clinical phenotyping in the digital era.

DOI：
10.1038/s41746-023-00878-9
发表时间：
2023-07-21
期刊：
NPJ digital medicine
影响因子：
15.2
作者：
通讯作者：

Clinical concept extraction: A methodology review.

DOI：
10.1016/j.jbi.2020.103526
发表时间：
2020-09
期刊：
Journal of biomedical informatics
影响因子：
4.5
作者：
Fu S;Chen D;He H;Liu S;Moon S;Peterson KJ;Shen F;Wang L;Wang Y;Wen A;Zhao Y;Sohn S;Liu H
通讯作者：
Liu H

Extracting chemical-protein relations using attention-based neural networks.

DOI：
10.1093/database/bay102
发表时间：
2018-01-01
期刊：
Database : the journal of biological databases and curation
影响因子：
0
作者：
Liu S;Shen F;Komandur Elayavilli R;Wang Y;Rastegar-Mojarad M;Chaudhary V;Liu H
通讯作者：
Liu H

Contextual Variation of Clinical Notes induced by EHR Migration.

EHR 迁移引起的临床记录的上下文变化。