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Curr Opin Crit Care. 2018 Jun;24(3):196-203. doi: 10.1097/MCC.0000000000000496.

Predicting adverse hemodynamic events in critically ill patients.

Author information

1
Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.

Abstract

PURPOSE OF REVIEW:

The 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 FINDINGS:

Data 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.

SUMMARY:

Using advanced analytic tools to glean knowledge from clinical data streams is rapidly becoming a reality whose clinical impact potential is great.

PMID:
29601321
PMCID:
PMC6007856
[Available on 2019-06-01]
DOI:
10.1097/MCC.0000000000000496
[Indexed for MEDLINE]

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