Tri-AL: An open source platform for visualization and analysis of clinical trials

IF 3 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems Pub Date : 2024-09-04 DOI:10.1016/j.is.2024.102459

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引用次数: 0

Abstract

ClinicalTrials.gov hosts an online database with over 440,000 medical studies (as of 2023) evaluating drugs, supplements, medical devices, and behavioral treatments. Target users include scientists, medical researchers, pharmaceutical companies, and other public and private institutions. Although ClinicalTrials has some filtering ability, it does not provide visualization tools, reporting tools or historical data; only the most recent state of each trial is visible to users. To fill these functionality gaps, we present Tri-AL: an open-source data platform for clinical trial visualization, information extraction, historical analysis, and reporting. This paper describes the design and functionality of Tri-AL, including a programmable module to incorporate machine learning models and extract disease-specific data from unstructured trial reports, which we demonstrate using Alzheimer’s disease reporting as a case study. We also highlight the use of Tri-AL for trial participation analysis in terms of sex, gender, race and ethnicity. The source code is publicly available at https://github.com/pouyan9675/Tri-AL.

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Tri-AL：用于临床试验可视化和分析的开源平台

ClinicalTrials.gov 是一个在线数据库，收录了超过 440,000 项评估药物、保健品、医疗器械和行为疗法的医学研究（截至 2023 年）。目标用户包括科学家、医学研究人员、制药公司以及其他公共和私营机构。尽管 ClinicalTrials 具有一定的筛选功能，但它不提供可视化工具、报告工具或历史数据；用户只能看到每个试验的最新状态。为了填补这些功能空白，我们提出了 Tri-AL：一个用于临床试验可视化、信息提取、历史分析和报告的开源数据平台。本文介绍了 Tri-AL 的设计和功能，包括一个可编程模块，用于整合机器学习模型，并从非结构化试验报告中提取特定疾病的数据。我们还重点介绍了如何使用 Tri-AL 从性别、种族和民族角度分析试验参与情况。源代码可通过 https://github.com/pouyan9675/Tri-AL 公开获取。

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

Information Systems 工程技术-计算机：信息系统

CiteScore

9.40

自引率

2.70%

发文量

112

审稿时长

53 days

期刊介绍： Information systems are the software and hardware systems that support data-intensive applications. The journal Information Systems publishes articles concerning the design and implementation of languages, data models, process models, algorithms, software and hardware for information systems. Subject areas include data management issues as presented in the principal international database conferences (e.g., ACM SIGMOD/PODS, VLDB, ICDE and ICDT/EDBT) as well as data-related issues from the fields of data mining/machine learning, information retrieval coordinated with structured data, internet and cloud data management, business process management, web semantics, visual and audio information systems, scientific computing, and data science. Implementation papers having to do with massively parallel data management, fault tolerance in practice, and special purpose hardware for data-intensive systems are also welcome. Manuscripts from application domains, such as urban informatics, social and natural science, and Internet of Things, are also welcome. All papers should highlight innovative solutions to data management problems such as new data models, performance enhancements, and show how those innovations contribute to the goals of the application.