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 |
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| Autores principales: | , |
| Formato: | research Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
Jun2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=129436170&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 129436170 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10705295 GGP jtl: Current Opinion in Critical Care issn: 10705295 maglogo: N pubinfo: dt: Jun2018 vid: 24 iid: 3 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 129436170 129436170 NLM29601321 129436170 10.1097/MCC.0000000000000496 NLM29601321 129436170 ppf: 196 ppct: 8 formats: tig: atl: Predicting adverse hemodynamic events in critically ill patients. aug: au: Yoon, Joo H. Pinsky, Michael R. affil: Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA sug: subj: Cardiovascular Diseases Diagnosis Critical Illness Respiration Disorders Diagnosis Hemodynamics Physiology Aged Middle Age Adult Respiration Disorders Physiopathology Cardiovascular Diseases Physiopathology Male Human Aged, 80 and Over Predictive Value of Tests Female Scales Aged: 65+ years Middle Aged: 45-64 years Adult: 19-44 years Aged, 80 & over Male Female ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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