Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review

Modern Intensive Care Units (ICUs) provide continuous monitoring of critically ill patients susceptible to many complications affecting morbidity and mortality. ICU settings require a high staff-to-patient ratio and generates a sheer volume of data. For clinicians, the real-time interpretation of da...

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Published inInformatics (Basel) Vol. 8; no. 1; p. 16
Main Authors Syed, Mahanazuddin, Syed, Shorabuddin, Sexton, Kevin, Syeda, Hafsa Bareen, Garza, Maryam, Zozus, Meredith, Syed, Farhanuddin, Begum, Salma, Syed, Abdullah Usama, Sanford, Joseph, Prior, Fred
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 01.03.2021
Subjects
Artificial intelligence
critical care
Datasets
Decision making
deep learning
Intensive care
intensive care unit
Literature reviews
Machine learning
MIMIC
Mortality
Neural networks
systematic review
deep learning
MIMIC
critical care
acute kidney injury
intensive care unit
machine learning
systematic review
sepsis
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Summary:Modern Intensive Care Units (ICUs) provide continuous monitoring of critically ill patients susceptible to many complications affecting morbidity and mortality. ICU settings require a high staff-to-patient ratio and generates a sheer volume of data. For clinicians, the real-time interpretation of data and decision-making is a challenging task. Machine Learning (ML) techniques in ICUs are making headway in the early detection of high-risk events due to increased processing power and freely available datasets such as the Medical Information Mart for Intensive Care (MIMIC). We conducted a systematic literature review to evaluate the effectiveness of applying ML in the ICU settings using the MIMIC dataset. A total of 322 articles were reviewed and a quantitative descriptive analysis was performed on 61 qualified articles that applied ML techniques in ICU settings using MIMIC data. We assembled the qualified articles to provide insights into the areas of application, clinical variables used, and treatment outcomes that can pave the way for further adoption of this promising technology and possible use in routine clinical decision-making. The lessons learned from our review can provide guidance to researchers on application of ML techniques to increase their rate of adoption in healthcare.
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Author Contributions: Conceptualization, M.S., F.P., and S.S.; methodology, M.S. and S.S.; formal analysis, all authors; investigation, all authors; resources, K.S.; data curation, all authors; writing—original draft preparation, M.S. and S.S.; writing—review and editing, all authors; validation, all authors; visualization, all authors; supervision, J.S.; project administration, A.U.S. and S.B.; funding acquisition, None. All authors have read and agreed to the published version of the manuscript.
These authors contributed equally to this work.
ISSN:2227-9709
2227-9709
DOI:10.3390/informatics8010016