The MIMIC Code Repository: enabling reproducibility in critical care research

By Alistair E. W. Johnson, David J. Stone, Leo Anthony Celi, Tom J. Pollard. 2017

430 citations recorded by the source corpus.

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.

Subfields: Epidemiology, Molecular Biology, Statistics and Probability

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Citations in the corpus

Evaluating Reinforcement Learning Algorithms in Observational Health Settings (2018, methodology): “We use a cohort of sepsis patients curated from the MIMIC III dataset [Johnson et al., 2017, Raghu et al., 2017].”
Representation Balancing MDPs for Off-Policy Policy Evaluation (2018, methodology): “Our work is inspired by recent advances[19, 12, 10, 11] in estimating conditional averaged treatment effects (CATE), also known as heterogeneous treatment effects (HTE), in the contextual bandit setting with a single (typically binary) action choice.”
MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports (2019, methodology): “We believe that publicly accessible code using publicly available data will accelerate research in the field and ensure reproducibility of future studies 26,27 .”
MIMIC-IV, a freely accessible electronic health record dataset (2023, methodology): “The MIMIC Code Repository was originally created for MIMIC-III to enable researchers to share and reuse code 24 .”