Predicting adverse hemodynamic events in critically ill patients.
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 inter...
| Publicado en: | Current Opinion in Critical Care Vol. 24; no. 3; pp. 196 - 204 |
|---|---|
| Autores principales: | , |
| Formato: | research Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
Jun2018
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | 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. |
|---|