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：
8928647
负责人：
HONGFANG LIU
金额：
$ 37.63万
依托单位：
MAYO CLINIC ROCHESTER
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 2019-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8928647
关键词：
Accounting Address Adopted Adoption Asthma Clinic Clinical Collection Communities Computerized Medical Record Computers Data Dictionary Disease Electronic Health Record Epidemiologist Epidemiology Evaluation Event Evidence Based Medicine Evolution Goals Health Information Retrieval Information Retrieval Systems Institution Interest Group Investigation Judgment Language Learning Machine Learning Measures Medical Medical Records Methodology Methods Modeling Modification Morphologic artifacts Names Natural Language Processing Outcome Patient Recruitments Patients Performance Pharmaceutical Preparations Phase Physicians Process Publishing Qualifying Records Research Research Personnel Resources Rest Retrieval Sampling Semantics Site Smoke Source Specific qualifier value Structure System Techniques Testing Text Validation Weight Work Writing asthmatic patient base cohort improved indexing novel open source query optimization syntax text searching tool

项目摘要

DESCRIPTION (provided by applicant): Natural Language Processing (NLP) techniques have shown promise for extracting data from the free text of electronic health records (EHRs), but studies have consistently found that techniques do not readily generalize across application settings. Unfortunately, most of the focus in applying NLP to real use cases has remained on a paradigm of single, well-defined application settings, so that generalizability to unseen use cases remains implicitly unaddressed. We propose to explicitly account for unseen application settings by adopting an information retrieval (IR) perspective with the objective of patient-level cohort identification. To do so, we introduce layered language models, an IR framework that enables the reuse of NLP-produced artifacts. Our long term goal is to accelerate investigations of patient health and disease by providing robust, user- centric tools that are necessary to process, retrieve, and utilize the free text of EHRs. The main goal of this proposal is to accurately retrieve ad hoc, realistic cohorts from clinical text at Mayo Clinic and OHSU, establishing methods, resources, and evaluation for patient-level IR. We hypothesize that cohort identification can be addressed in a generalizable fashion by a new IR framework: layered language models. We will test this hypothesis through four specific aims. In Aim 1, we will make medical NLP artifacts searchable in our layered language IR framework. This involves storing and indexing the NLP artifacts, as well as using statistical language models to retrieve documents based on text and its associated NLP artifacts. In Aim 2, we deal with the practical setting of ad hoc cohort identification, moving to patient-level (rather than document-level) IR. To accurately handle patient cohorts in which qualifying evidence may be spread over multiple documents, we will develop and implement patient-level retrieval models that account for cross- document relational and temporal combinations of events. In Aim 3, we will construct parallel IR test collections using EHR data from two sites; a diverse set of cohort queries written by multiple people toward various clinical or epidemiological ends; and assessments of which patients are relevant to which queries at both sites. Finally, in Aim 4, we refine and evaluate patient-level layered language IR on the ad hoc cohort identification task, making comparisons across the users, queries, optimization metrics, and institutions. We will draw additional extrinsic comparisons with pre-existing techniques, e.g., for cohorts from the Electronic Medical Records and Genonmics network. The expected outcomes of the proposed work are: (i) An open-source cohort identification tool, usable by clinicians and epidemiologists, that makes principled use of NLP artifacts for unseen queries; ii) A parallel test collection for cohort identification, includig two intra-institutional document collections, diverse test topics and user-produced text queries, and patient-level judgments of relevance to each query; and (iii) Validation of the reusability of medical NLP via the task of retrieving patient cohorts.

描述（由申请人提供）：自然语言处理（NLP）技术已经显示出从电子健康记录（EHR）自由文本中提取数据的希望，但是研究一直发现，技术并不能跨应用程序设置概述。不幸的是，将NLP应用于真实用例的大多数重点仍然存在于单个，定义明确的应用程序设置的范式上，因此，未见用例的概括性仍然隐含地未受解答。我们建议通过采用信息检索（IR）观点来明确说明未见的申请设置，以患者级同类识别的目的。为此，我们介绍了分层语言模型，这是一个IR框架，可重用NLP生产的工件。我们的长期目标是通过提供可加工，以用户为中心的工具来加快对患者健康和疾病的调查，这些工具是处理，检索和利用EHR的自由文本所必需的。该提案的主要目的是准确地从Mayo Clinic和OHSU的临床文本中准确检索临时人群，建立患者级IR的方法，资源和评估。我们假设可以通过新的IR框架：分层语言模型以可推广的方式来解决队列识别。我们将通过四个特定目标检验这一假设。在AIM 1中，我们将在我们的分层IR框架中使医疗NLP工件可以搜索。这涉及存储和索引NLP工件，以及使用统计语言模型根据文本及其相关的NLP工件检索文档。在AIM 2中，我们处理了临时组合识别的实际设置，转向患者级（而不是文档级）IR。为了准确处理可能分配到多个文档的合格证据的患者同类，我们将开发和实施患者级检索模型，以解释事件的跨文档关系和时间组合。在AIM 3中，我们将使用来自两个站点的EHR数据构建并行的IR测试收集。由多个撰写的一组各种队列查询人们走向各种临床或流行病学末端；以及评估哪些患者与两个部位的疑问有关。最后，在AIM 4中，我们在临时同步识别任务上完善和评估患者级分层语言IR，从而对用户，查询，优化指标和机构进行比较。我们将与先前存在的技术进行其他外部比较，例如电子病历和GenOnmics网络的同伙。拟议作品的预期结果是：（i）一种开源队列识别工具，可供临床医生和流行病学家使用，该工具可将NLP工件原则地用于看不见的查询； ii）用于队列识别的并行测试收集，包括两个机构内的文件集合，不同的测试主题和用户生产的文本查询以及与每个查询相关的患者级别的判断；（iii）通过检索患者队列的任务来验证医疗NLP的可重复性。