A Critical Look at a Critical Care Dataset: MIMIC-IV's Construction, Contents, & Consequences

MIMIC (Medical Information Mart for Intensive Care) is one of the largest, most commonly-used, freely available datasets containing intensive care unit data. I conduct denotative, connotative, and deconstructive readings of the MIMIC-IV dataset through an analysis of the data sources, dataset structure, and the process for getting access to the data, as well as documents and concepts related to the dataset. As a result, I demonstrate that the MIMIC-IV dataset requires more documentation, including an expansion of the existing descriptions, in order to ensure the data is used appropriately and allow for maximum benefit. I make recommendations for future users of the MIMIC-IV dataset, creators of datasets in general, and researchers in the Critical Data Studies field based on my findings.

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Publication Details

Journal
Proceedings of the AAAI/ACM Conference on AI Ethics and Society
Published
2025-10-15
DOI
https://doi.org/10.1609/aies.v8i1.36550
Primary Topic
Primary Care and Health Outcomes
Type
article
Field-Weighted Citation Impact
0.00
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article

A Critical Look at a Critical Care Dataset: MIMIC-IV's Construction, Contents, & Consequences

Pınar Barlas
Proceedings of the AAAI/ACM Conference on AI Ethics and Society
Primary Care and Health Outcomes
article

A Critical Look at a Critical Care Dataset: MIMIC-IV's Construction, Contents, & Consequences

Pınar Barlas
article en

Abstract

MIMIC (Medical Information Mart for Intensive Care) is one of the largest, most commonly-used, freely available datasets containing intensive care unit data. I conduct denotative, connotative, and deconstructive readings of the MIMIC-IV dataset through an analysis of the data sources, dataset structure, and the process for getting access to the data, as well as documents and concepts related to the dataset. As a result, I demonstrate that the MIMIC-IV dataset requires more documentation, including an expansion of the existing descriptions, in order to ensure the data is used appropriately and allow for maximum benefit. I make recommendations for future users of the MIMIC-IV dataset, creators of datasets in general, and researchers in the Critical Data Studies field based on my findings.

Proceedings of the AAAI/ACM Conference on AI Ethics and SocietyVol. 8(1)
Western University (CA)
Openalex Percentile: Top 54%
Primary Care and Health Outcomes
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