Application of machine learning in intensive care unit (ICU) settings using MIMIC dataset: Systematic review

Mahanazuddin Syed, Shorabuddin Syed, Kevin Sexton, Hafsa Bareen Syeda, Maryam Garza, Meredith Zozus, Farhanuddin Syed, Salma Begum, Abdullah Usama Syed, Joseph Sanford, Fred Prior

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article number16
JournalInformatics
Volume8
Issue number1
DOIs
StatePublished - Mar 2021

Keywords

  • Acute kidney injury
  • Critical care
  • Deep learning
  • Intensive care unit
  • Machine learning
  • MIMIC
  • Sepsis
  • Systematic review

ASJC Scopus subject areas

  • Communication
  • Human-Computer Interaction
  • Computer Networks and Communications

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