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

Identification of data elements for blood gas analysis dataset: a base for developing registries and artificial intelligence-based systems

“…Despite the wide adoption of AI-based applications, such as machine learning in ICUs, to our knowledge, this is the first developed dataset of data elements required for comprehensive BGA. However, according to the systematic reviews performed by Syed et al and Shillan et al [ 31 , 32 ], machine learning applications are widely applied for predicting ICU mortality, readmission, acute kidney injury, and sepsis. Although advances in AI-bassed techniques have turned from “a future possibility” to an “everyday reality” for managing patients in ICUs, there are still challenges in the usage of these systems [ 33 ].…”

“…Developing AI-based systems requires large datasets for modeling complex and non-linear effects or developing evidence-based algorithms [34,35]. In an attempt to cover this issue in intensive care, Johnson et al [25] released the Medical Information Mart for Intensive Care (MIMIC-III) dataset that allows researchers to solve complex healthcare problems through developing electronic systems [31]. For instance, through extracting relevant features from the MIMIC-III dataset, Yang et al [36] proposed an algorithm based on the noninvasive physiological parameters of patients to calculate the partial pressure of oxygen/fraction of inspired oxygen (PaO 2 /FiO 2 ) ratio for the identification of patients with acute respiratory distress syndrome.…”

MiPy: A Framework for Benchmarking Machine Learning Prediction of Unplanned Hospital and ICU Readmission in the MIMIC-IV Database

“…The scale, accessibility and popularity among researchers of this database makes it a perfect tool for comparison and benchmarking of different machine learning approaches. There are a multitude of research on the previous version of the database [17,19,31], however MIMIC-IV is still relatively new and unexplored.…”

Machine Learning Approach to Optimize Sedation Use in Endoscopic Procedures

“…Artificial Intelligence (AI) techniques including Machine Learning (ML) and Deep Learning (DL) are being used in a variety of clinical applications ranging from diagnosis to outcome prediction [ 5 – 7 ]. ML is an iterative process in which the algorithm tries to find the optimal combination of both model variables and variable weights with the goal of minimizing error in the predicted outcome [ 8 ].…”