کلان داده و علوم داده در مراقبت های ویژه Big Data and Data Science in Critical Care

INTRODUCTION The digitalization of the healthcare system is changing the way we practice medicine and conduct clinical research.1,2 The widespread implementation of electronic health records (EHRs) is paving the way for Big Data research and is bringing the world of data science to the patient’s bedside.2-4 Within the healthcare system, the intensive care unit (ICU) presents a particularly convincing case for using data science to improve patient care.5 For one, the evidence supporting many of the interventions performed in the ICU is rather scarce and practice variability is abundant.5,6 In addition, the complexity of critical illness makes the traditional reductionist approach to medical research insufficient, that is, single drug intervention trials or single pathway biomarker studies are unlikely to satisfy the clinical realities of the ICU.5 Critical care research requires an integrative approach that embraces the complexity of critical illness and the computational technology and algorithms that can make it possible.7,8 Moreover, the data required to do this are being generated and digitized in troves. EHRs, bedside monitors, medication pumps, and ventilators are continuously generating new minable data, and soon the advancement of modern molecular diagnostics will result in a deluge of “omics” data derived from the genome, transcriptome, microbiome, and a long list of other “-omes” (Figure 1). As Big Data and data science gradually infiltrate most aspects of clinical research and – ultimately– clinical care in the ICU, it is increasingly evident that intensivists should be familiar with the promise and perils of these approaches. In this paper, we will review the definitions, types of algorithms, applications, challenges, and future of Big Data and data science in critical care.