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

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

doi:10.3390/informatics8010016

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.

Highlights

Artificial intelligence (AI) encompasses a broad-spectrum of technologies that aim to imitate cognitive functions and intelligent behavior of humans [1]
This study focused on peer-reviewed publications that applied Machine Learning (ML) techniques to analyze retrospective Intensive Care Units (ICUs) data available from publicly available Medical Information Mart for Intensive Care (MIMIC) dataset, which includes discrete structured clinical data, physiological waveforms data, free text documents, and radiology imaging reports
61 publications were selected for further analyses. These publications were categorized into seven themes based on the effectiveness of applying ML techniques in various ICU settings

Summary

Introduction

Artificial intelligence (AI) encompasses a broad-spectrum of technologies that aim to imitate cognitive functions and intelligent behavior of humans [1]. Machine Learning (ML) is a subfield of AI that focuses on algorithms that allow computers to define a model for complex relationships or patterns from empirical data without being explicitly programmed [2]. ML, powered by increasing availability of healthcare data, is being used in a variety of clinical applications ranging from diagnosis to outcome prediction [1,3]. The predictive power of ML improves as the number of samples available for learning increases [4,5]. ML algorithms can be supervised or unsupervised based on the type of learning rule employed. An algorithm is trained using well-labeled data

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