评估和扩展知情同意本体,用于表示临床领域的许可。

IF 2.5 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Applied Ontology Pub Date : 2022-01-01 Epub Date: 2022-05-04 DOI:10.3233/ao-210260
Elizabeth E Umberfield, Cooper Stansbury, Kathleen Ford, Yun Jiang, Sharon L R Kardia, Andrea K Thomer, Marcelline R Harris
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引用次数: 0

摘要

本研究的目的是评估、修订和扩展知情同意本体(ICO),以表达临床权限,包括剩余临床生物样本和健康数据的再利用。本研究采用了形成性评估设计,并使用了自下而上的建模方法。数据来自有关美国联邦法规的文献和对临床同意书的研究。对 11 项联邦法规和临床同意书中的 15 个许可句子进行了反复建模,以确定实体及其关系,然后根据一系列预先确定的评估问题进行社区反思和协商。ICO 包括建模时所需的 52 个类和 12 个对象属性,这表明 ICO 的扩展适用于临床领域。另外还从其他本体论中导入了 26 个类到 ICO 中,并建议开发 12 个新类。这项工作填补了正式表述临床权限(包括残留临床生物样本和健康数据的再利用)方面的一个重要空白。它将缺失的内容提供给 OBO 基金会,使其能够与其他广泛采用的生物医学本体论一起使用。ICO 可作为一种机器可解释和可互操作的工具,用于大规模负责任地再利用残留临床生物样本和健康数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Evaluating and Extending the Informed Consent Ontology for Representing Permissions from the Clinical Domain.

Evaluating and Extending the Informed Consent Ontology for Representing Permissions from the Clinical Domain.

Evaluating and Extending the Informed Consent Ontology for Representing Permissions from the Clinical Domain.

Evaluating and Extending the Informed Consent Ontology for Representing Permissions from the Clinical Domain.

The purpose of this study was to evaluate, revise, and extend the Informed Consent Ontology (ICO) for expressing clinical permissions, including reuse of residual clinical biospecimens and health data. This study followed a formative evaluation design and used a bottom-up modeling approach. Data were collected from the literature on US federal regulations and a study of clinical consent forms. Eleven federal regulations and fifteen permission-sentences from clinical consent forms were iteratively modeled to identify entities and their relationships, followed by community reflection and negotiation based on a series of predetermined evaluation questions. ICO included fifty-two classes and twelve object properties necessary when modeling, demonstrating appropriateness of extending ICO for the clinical domain. Twenty-six additional classes were imported into ICO from other ontologies, and twelve new classes were recommended for development. This work addresses a critical gap in formally representing permissions clinical permissions, including reuse of residual clinical biospecimens and health data. It makes missing content available to the OBO Foundry, enabling use alongside other widely-adopted biomedical ontologies. ICO serves as a machine-interpretable and interoperable tool for responsible reuse of residual clinical biospecimens and health data at scale.

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来源期刊
Applied Ontology
Applied Ontology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
4.80
自引率
30.00%
发文量
15
审稿时长
>12 weeks
期刊介绍: Applied Ontology focuses on information content in its broadest sense. As the subtitle makes clear, two broad kinds of content-based research activities are envisioned: ontological analysis and conceptual modeling. The former includes any attempt to investigate the nature and structure of a domain of interest using rigorous philosophical or logical tools; the latter concerns the cognitive and linguistic structures we use to model the world, as well as the various analysis tools and methodologies we adopt for producing useful computational models, such as information systems schemes or knowledge structures. Applied Ontology is the first journal with explicit and exclusive focus on ontological analysis and conceptual modeling under an interdisciplinary view. It aims to establish a unique niche in the realm of scientific journals by carefully avoiding unnecessary duplication with discipline-oriented journals. For this reason, authors will be encouraged to use language that will be intelligible also to those outside their specific sector of expertise, and the review process will be tailored to this end. For example, authors of theoretical contributions will be encouraged to show the relevance of their theory for applications, while authors of more technological papers will be encouraged to show the relevance of a well-founded theoretical perspective. Moreover, the journal will publish papers focusing on representation languages or algorithms only where these address relevant content issues, whether at the level of practical application or of theoretical understanding. Similarly, it will publish descriptions of tools or implemented systems only where a contribution to the practice of ontological analysis and conceptual modeling is clearly established.
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