Biomedical Terminology Quality Assurance for Enhancing Clinical Queries over Electronic Health Records

增强电子健康记录临床查询的生物医学术语质量保证

基本信息

  • 批准号:
    10226835
  • 负责人:
  • 金额:
    $ 33.15万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2020
  • 资助国家:
    美国
  • 起止时间:
    2020-08-01 至 2023-07-31
  • 项目状态:
    已结题

项目摘要

PROJECT SUMMARY We propose to develop an automatic change-suggestion (auto-suggestion) approach for quality enhancement of biomedical terminologies. This approach can not only detect errors, but also suggest changes that lead to the identification and fixes of the root causes of errors. Biomedical terminologies provide the basis for data quality in data collection, annotation, management, analysis, sharing, and reuse. They not only serve as a part of the metadata standards for describing data in the FAIR Data Principles (Findable, Accessible, Interoperable, Reusable), but also play a vital role in downstream information systems as a declarative knowledge source. Because of these and additional new roles biomedical terminologies may play, quality issues, if not addressed, can affect the quality of all downstream information systems and tools (including electronic health record, clinical decision support and patient safety evaluation systems). Most existing terminology quality assurance approaches merely indicate the presence of possible quality issues but do not automatically provide suggestion for fixes. The long-term goal of this study is to develop an approach for AutomatiC Error- identification and change-Suggestion (ACES), moving domain expert and ontology engineer's effort to validating suggested changes, rather than creating changes. To advance this goal, we propose three specific aims: Aim 1. To develop an auto-suggestion reasoning framework for automatic error detection in non- lattice subgraphs by performing Formal Concept Analysis (FCA) on logical definitions of concepts. The constructed FCA-lattices will serve as logically meaningful reference structures for comparison with the original non-lattice subgraphs to automatically reveal potential errors as well as suggest remedies. Aim 2. To develop an automated method to uncover root causes of errors in logical definitions of concepts and suggest remedial changes in the definitions for evaluation. We will develop a reasoning algorithm to automate the process of locating erroneous or incomplete logical definitions that lead to the potential errors. Working with domain experts, we will evaluate randomly selected auto-suggestions using our web-based system to assess the effectiveness of our error detection and root-cause analysis methods. Aim 3. To quantitatively assess the terminology quality impact on queries over healthcare data for patient cohort identification. We will leverage SNOMED CT and a comprehensive EHR database Cerner Health Facts® to measure the global impact of missing is-a relations and incorrect is-a relations on performing clinical queries over the EHR database (missing is-a relations reduce recalls of queries, and incorrect is-a relations reduce the precisions of queries). Our utilization of non-lattice subgraphs is based on a rigorous mathematical theory, which suggests that the hierarchical relation between ontological concepts should structurally conform to the mathematical property of being a lattice. Therefore, ACES is generalizable to virtually all biomedical terminologies, and the expected impact is high.
项目摘要 我们建议开发一种自动更改建议(自动建议)方法,以提高质量 生物医学术语。这种方法不仅可以检测错误,还可以建议导致 识别和修复错误的根本原因。生物医学术语为数据提供了基础 数据收集、注释、管理、分析、共享和再利用的质量。它们不仅是 在FAIR数据原则中描述数据的元数据标准(可查找,可解释,可互操作, 可重用),而且在下游信息系统中作为声明性知识源也发挥着至关重要的作用。 由于这些和其他新的作用,生物医学术语可能发挥,质量问题,如果不解决, 可能影响所有下游信息系统和工具的质量(包括电子健康记录, 临床决策支持和患者安全评估系统)。大多数现有术语质量保证 方法仅指示可能存在的质量问题,但不会自动提供 建议修复。本研究的长期目标是开发一种自动错误的方法- 识别和更改建议(ACES),将领域专家和本体工程师努力 验证建议的更改,而不是创建更改。为了实现这一目标,我们提出三个 具体目标:目标1。开发一个自动暗示推理框架,用于非语言环境中的自动错误检测。 通过对概念的逻辑定义执行形式概念分析(FCA)来分析格子子图。的 构造的FCA格将作为逻辑上有意义的参考结构,用于与原始结构进行比较 非格子子图,以自动揭示潜在的错误以及建议补救措施。目标2.发展 一种自动化方法,用于发现概念逻辑定义中错误的根本原因,并提出补救建议 评价定义的变化。我们将开发一个推理算法来自动化的过程, 定位导致潜在错误的错误或不完整的逻辑定义。使用域 专家,我们将评估随机选择的自动建议使用我们的基于网络的系统,以评估 我们的错误检测和根本原因分析方法的有效性。目标3。定量评价 术语质量对医疗保健数据查询的影响,用于患者队列识别。我们将利用 SNOMED CT和一个全面的EHR数据库Cerner Health Facts®,以衡量 在EHR数据库上执行临床查询时缺少is-a关系和is-a关系不正确 (丢失的IS-A关系减少查询的召回,而不正确的IS-A关系减少查询的精确度)。 我们利用非格子子图是基于严格的数学理论,这表明, 本体论概念之间的层次关系在结构上应该符合 作为一个格子。因此,ACES可推广到几乎所有的生物医学术语,并且预期 影响是高的。

项目成果

期刊论文数量(15)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
An ontology-based approach for harmonization and cross-cohort query of Alzheimer's disease data resources.
Identifying Missing IS-A Relations in Orphanet Rare Disease Ontology
识别孤儿罕见疾病本体中缺失的 IS-A 关系
A GCN-based approach to uncover misaligned synonymous terms in the UMLS Metathesaurus.
一种基于 GCN 的方法,用于发现 UMLS Metathesaurus 中未对齐的同义词术语。
A Query Engine for Self-controlled Case Series: with an application to COVID-19 EHR data
用于自我控制案例系列的查询引擎:适用于 COVID-19 EHR 数据
Automated Identification of Missing IS-A Relations in the Human Phenotype Ontology.
自动识别人类表型本体中缺失的 IS-A 关系。
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Licong Cui其他文献

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{{ truncateString('Licong Cui', 18)}}的其他基金

An Interface Ontology for Alzheimer's Disease Research
阿尔茨海默病研究的界面本体
  • 批准号:
    10042812
  • 财政年份:
    2020
  • 资助金额:
    $ 33.15万
  • 项目类别:
An informatics framework for SUDEP Risk Marker Identification and Risk Assessment
SUDEP 风险标记识别和风险评估的信息学框架
  • 批准号:
    10614940
  • 财政年份:
    2020
  • 资助金额:
    $ 33.15万
  • 项目类别:
An informatics framework for SUDEP Risk Marker Identification and Risk Assessment
SUDEP 风险标记识别和风险评估的信息学框架
  • 批准号:
    10163933
  • 财政年份:
    2020
  • 资助金额:
    $ 33.15万
  • 项目类别:
An informatics framework for SUDEP Risk Marker Identification and Risk Assessment
SUDEP 风险标记识别和风险评估的信息学框架
  • 批准号:
    10393043
  • 财政年份:
    2020
  • 资助金额:
    $ 33.15万
  • 项目类别:
An Interface Ontology for Alzheimer's Disease Research
阿尔茨海默病研究的界面本体
  • 批准号:
    10261454
  • 财政年份:
    2020
  • 资助金额:
    $ 33.15万
  • 项目类别:

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