A computable biomedical knowledge system: Toward rapidly building candidate-directed acyclic graphs

IF 3.6 2区医学 Q1 MEDICINE, GENERAL & INTERNAL

Journal of Evidence‐Based Medicine Pub Date : 2024-03-31 DOI:10.1111/jebm.12602

Yongmei Bai, Xuanyu Shi, Jian Du

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引用次数: 0

Abstract

Aim

It is essential for health researchers to have a systematic understanding of third-party variables that influence both the exposure and outcome under investigation, as shown by a directed acyclic graph (DAG). The traditional construction of DAGs through literature review and expert knowledge often needs to be more systematic and consistent, leading to potential biases. We try to introduce an automatic approach to building network linking variables of interest.

Methods

Large-scale text mining from medical literature was utilized to construct a conceptual network based on the Semantic MEDLINE Database (SemMedDB). SemMedDB is a PubMed-scale repository of the “concept-relation-concept” triple format. Relations between concepts are categorized as Excitatory, Inhibitory, or General.

Results

To facilitate the use of large-scale triple sets in SemMedDB, we have developed a computable biomedical knowledge (CBK) system (https://cbk.bjmu.edu.cn/), a website that enables direct retrieval of related publications and their corresponding triples without the necessity of writing SQL statements. Three case studies were elaborated to demonstrate the applications of the CBK system.

Conclusions

The CBK system is openly available and user-friendly for rapidly capturing a set of influencing factors for a phenotype and building candidate DAGs between exposure-outcome variables. It could be a valuable tool to reduce the exploration time in considering relationships between variables, and constructing a DAG. A reliable and standardized DAG could significantly improve the design and interpretation of observational health research.

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可计算的生物医学知识系统：快速构建候选定向非循环图。

目的：正如有向无环图（DAG）所示，健康研究人员必须系统地了解影响调查对象暴露和结果的第三方变量。传统的通过文献综述和专家知识构建 DAG 的方法往往需要更加系统和一致，从而导致潜在的偏差。我们尝试引入一种自动方法来构建连接相关变量的网络：方法：利用对医学文献的大规模文本挖掘来构建基于语义MEDLINE数据库（SemMedDB）的概念网络。SemMedDB是一个PubMed规模的 "概念-关系-概念 "三重格式存储库。概念之间的关系被分为兴奋性、抑制性或一般性：为了便于在 SemMedDB 中使用大规模三元组，我们开发了一个可计算生物医学知识（CBK）系统（https://cbk.bjmu.edu.cn/），这是一个无需编写 SQL 语句即可直接检索相关出版物及其相应三元组的网站。我们详细阐述了三个案例研究，以展示 CBK 系统的应用：CBK 系统可公开使用，用户界面友好，可快速捕捉表型的一系列影响因素，并在暴露-结果变量之间建立候选 DAG。它可以作为一种宝贵的工具，减少考虑变量间关系和构建 DAG 的探索时间。可靠和标准化的 DAG 可以极大地改进观察性健康研究的设计和解释。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Evidence‐Based Medicine MEDICINE, GENERAL & INTERNAL-

CiteScore

11.20

自引率

1.40%

发文量

期刊介绍： The Journal of Evidence-Based Medicine (EMB) is an esteemed international healthcare and medical decision-making journal, dedicated to publishing groundbreaking research outcomes in evidence-based decision-making, research, practice, and education. Serving as the official English-language journal of the Cochrane China Centre and West China Hospital of Sichuan University, we eagerly welcome editorials, commentaries, and systematic reviews encompassing various topics such as clinical trials, policy, drug and patient safety, education, and knowledge translation.