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MIMIC 代码库:实现重症监护研究的可重复性。

The MIMIC Code Repository: enabling reproducibility in critical care research.

机构信息

Massachusetts Institute of Technology, Cambridge, MA, USA.

University of Virginia School of Medicine, Charlottesville, VA, USA.

出版信息

J Am Med Inform Assoc. 2018 Jan 1;25(1):32-39. doi: 10.1093/jamia/ocx084.

Abstract

OBJECTIVE

Lack of reproducibility in medical studies is a barrier to the generation of a robust knowledge base to support clinical decision-making. In this paper we outline the Medical Information Mart for Intensive Care (MIMIC) Code Repository, a centralized code base for generating reproducible studies on an openly available critical care dataset.

MATERIALS AND METHODS

Code is provided to load the data into a relational structure, create extractions of the data, and reproduce entire analysis plans including research studies.

RESULTS

Concepts extracted include severity of illness scores, comorbid status, administrative definitions of sepsis, physiologic criteria for sepsis, organ failure scores, treatment administration, and more. Executable documents are used for tutorials and reproduce published studies end-to-end, providing a template for future researchers to replicate. The repository's issue tracker enables community discussion about the data and concepts, allowing users to collaboratively improve the resource.

DISCUSSION

The centralized repository provides a platform for users of the data to interact directly with the data generators, facilitating greater understanding of the data. It also provides a location for the community to collaborate on necessary concepts for research progress and share them with a larger audience. Consistent application of the same code for underlying concepts is a key step in ensuring that research studies on the MIMIC database are comparable and reproducible.

CONCLUSION

By providing open source code alongside the freely accessible MIMIC-III database, we enable end-to-end reproducible analysis of electronic health records.

摘要

目的

医学研究的可重复性缺乏是阻碍建立稳健知识库以支持临床决策的一个障碍。本文概述了医疗信息监护模拟数据库(MIMIC)代码库,这是一个中央代码库,用于在公开可用的重症监护数据集上生成可重复的研究。

材料与方法

提供了代码以将数据加载到关系结构中,创建数据提取,并重现包括研究在内的整个分析计划。

结果

提取的概念包括疾病严重程度评分、合并症状态、脓毒症的行政定义、脓毒症的生理标准、器官衰竭评分、治疗管理等。可执行文档用于教程,并端到端重现已发表的研究,为未来的研究人员提供了复制的模板。存储库的问题跟踪器使数据使用者能够与数据生成者直接交互,从而更好地理解数据。它还为社区提供了一个协作平台,以推进研究所需的概念,并与更广泛的受众分享这些概念。对底层概念应用相同的代码是确保 MIMIC 数据库上的研究具有可比性和可重复性的关键步骤。

结论

通过提供开源代码以及免费访问的 MIMIC-III 数据库,我们实现了电子健康记录的端到端可重复分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d7a/6381763/26cdfa7f4cd0/ocx084f1.jpg

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