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

项目总结 我们建议开发一种自动更改建议(自动建议)方法来提高质量 生物医学术语。这种方法不仅可以检测错误，还可以建议导致以下方面的更改 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、共享和重复使用的质量。它们不仅是一部分 以公平数据原则描述数据的元数据标准(可查找、可访问、可互操作、 可重复使用)，但也作为说明性知识来源在下游信息系统中发挥重要作用。 由于这些和其他新的生物医学术语可能扮演的角色，如果不解决质量问题， 会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)。大多数现有术语的质量保证 方法仅指示可能存在的质量问题，但不会自动提供 关于修复的建议。这项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(ACE)，移动领域专家和本体工程师的努力 验证建议的更改，而不是创建更改。为了推进这一目标，我们提出了三点建议 具体目标：目标1.开发一个自动建议推理框架，用于自动错误检测 通过对概念的逻辑定义执行形式概念分析(FCA)来生成格子图。这个 所构造的FCA-格将作为逻辑上有意义的参考结构与原始FCA-格进行比较 非格子图自动显示潜在的错误以及建议的补救措施。目标2.发展 一种自动方法，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 评估定义中的变化。我们将开发一种推理算法来自动处理 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，我们将使用我们的基于网络的系统对随机选择的自动建议进行评估 我们的错误检测和根本原因分析方法的有效性。目标3.定量评估 术语质量对查询医疗保健数据以确定患者队列的影响。我们将利用 SNOMED CT和全面的EHR数据库Cerner Health Fact®，以衡量 在EHR数据库上执行临床查询时缺少IS-a关系和不正确的IS-a关系 (缺失的is-a关系降低了查询的召回率，不正确的is-a关系降低了查询的精确度)。 我们对非格子图的使用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符合 作为一个格子。因此，ACE几乎可以概括到所有生物医学术语，并且预期 影响很大。

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.