• Joo H Yoon and Michael R Pinsky.
• Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
• Curr Opin Crit Care. 2018 Jun 1; 24 (3): 196-203.

Purpose Of ReviewThe art of predicting future hemodynamic instability in the critically ill has rapidly become a science with the advent of advanced analytical processed based on computer-driven machine learning techniques. How these methods have progressed beyond severity scoring systems to interface with decision-support is summarized.Recent FindingsData mining of large multidimensional clinical time-series databases using a variety of machine learning tools has led to our ability to identify alert artifact and filter it from bedside alarms, display real-time risk stratification at the bedside to aid in clinical decision-making and predict the subsequent development of cardiorespiratory insufficiency hours before these events occur. This fast evolving filed is primarily limited by linkage of high-quality granular to physiologic rationale across heterogeneous clinical care domains.SummaryUsing advanced analytic tools to glean knowledge from clinical data streams is rapidly becoming a reality whose clinical impact potential is great.

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