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BACKGROUND The Medical Information Mart for Intensive Care (MIMIC) database has significantly advanced research in critical care and biomedical informatics with its open, comprehensive dataset. This bibliometric analysis examines the evolution, impact, and thematic trends of MIMIC database research from 2003 to 2023, providing insights into its developmental trajectory and the current scientific landscape. OBJECTIVE This study aims to bridge this gap by employing bibliometric methods to dissect MIMIC-related literature indexed in the Web of Science, uncovering the database's development trends and focal research areas, thereby illuminating the path for future investigations in this vital field. METHODS This analysis evaluated 1,796 publications from the MIMIC database over 2003-2023. It synthesized data on publication trends, authorship patterns, institutional contributions, and thematic developments. Publications were assessed for frequency, citation counts, and keyword occurrences to highlight significant research trends and influential works. RESULTS The introduction of MIMIC-III and MIMIC-IV marked significant enhancements in data quality and accessibility, correlating with increased research output, especially post-2015. Prominent contributors included Jinan University, Zhejiang University School of Medicine, and MIT. Key journals from Switzerland, the UK, and the US dominated the dissemination of MIMIC-related research. Predominant themes were mortality, intensive care, and sepsis, with growing interests in machine learning and AI in critical care. CONCLUSIONS The MIMIC database has catalyzed a dynamic and evolving research landscape that mirrors broader trends in medical informatics and critical care. The marked increase in publications after recent database updates highlights rapid advancements in the field. Although limited to English-language articles indexed in the Web of Science Core Collection, this study provides a foundational understanding of key research areas and suggests pathways for future interdisciplinary applications of the MIMIC database.
Zhang et al. (Fri,) studied this question.