Big Data and Data Science in Critical Care

Abstract

The digitalization of the health-care system has resulted in a deluge of clinical big data and has prompted the rapid growth of data science in medicine. Data science, which is the field of study dedicated to the principled extraction of knowledge from complex data, is particularly relevant in the critical care setting. The availability of large amounts of data in the ICU, the need for better evidence-based care, and the complexity of critical illness makes the use of data science techniques and data-driven research particularly appealing to intensivists. Despite the increasing number of studies and publications in the field, thus far there have been few examples of data science projects that have resulted in successful implementations of data-driven systems in the ICU. However, given the expected growth in the field, intensivists should be familiar with the opportunities and challenges of big data and data science. The present article reviews the definitions, types of algorithms, applications, challenges, and future of big data and data science in critical care.

Keywords: big data; critical care; data science; machine learning; prediction models.

Figure 1

Some of the major sources of big data in the ICU. The term “omics” refers to the data derived from modern molecular techniques (eg, genomics, transcriptomics, proteomics, metabolomics, microbiomics). EHR = electronic health record.

Figure 2

A-C, Types of machine learning algorithms applicable to critical care. A, Supervised learning algorithms can be used, for example, to uncover the relationship between patient clinical features (eg, laboratory tests and vital signs) and mortality to predict the outcome in future cases. B, Unsupervised learning algorithms can be used to uncover naturally occurring groupings or clusters of patients based on their clinical characteristics, without targeting a specific outcome. C, Deep learning algorithms can be used, for example, to extract meaningful features from imaging data (eg, chest radiograph) to represent information in an increasingly higher order of hierarchical complexity and be able to make predictions, such as the presence of pathologic findings.