Machine Learning Methods to Predict Acute Respiratory Failure and Acute Respiratory Distress Syndrome - Wong, An-Kwok Ian; Cheung, Patricia C.; Kamaleswaran, Rishikesan; Martin, Greg S.; Holder, Andre L. (Frontiers in Big Data, 2020)
Acute respiratory failure is a common problem in medicine that utilizes significant healthcare resources, and is associated with high morbidity and mortality. Classification of acute respiratory failure is complicated, and is often determined by the level of mechanical support that is required, or the discrepancy between oxygen supply and uptake. These phenotypes make acute respiratory failure a continuum of syndromes, rather than one homogenous disease process. Early recognition of the risk factors for new or acute respiratory failure may prevent that process from occurring or worsening. Predictive analytical methods using machine learning leverage clinical data to provide an early warning for impending acute respiratory failure or its sequelae. The aims of this review are to summarize the current literature on acute respiratory failure prediction, show the gaps in what is currently known about this domain, demonstrate the issues and challenges with respiratory failure prediction, and offer potential paths forward for early detection of acute respiratory failure to improve clinical outcomes.