Improved predictive models for acute kidney injury with IDEA: Intraoperative Data Embedded Analytics

Acute kidney injury (AKI) is a common complication after surgery that is associated with increased morbidity and mortality. The majority of existing perioperative AKI risk prediction models are limited in their generalizability and do not fully utilize intraoperative physiological time-series data....

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	PloS one 2019-04, Vol.14 (4), p.e0214904
Hauptverfasser:	Adhikari, Lasith, Ozrazgat-Baslanti, Tezcan, Ruppert, Matthew, Madushani, R W M A, Paliwal, Srajan, Hashemighouchani, Haleh, Zheng, Feng, Tao, Ming, Lopes, Juliano M, Li, Xiaolin, Rashidi, Parisa, Bihorac, Azra
Format:	Artikel
Sprache:	eng
Schlagworte:	Acute kidney failure Acute Kidney Injury - etiology Adult Adults Aged Algorithms Analytics Anesthesiology Area Under Curve Biology and Life Sciences Blood pressure Cardiovascular diseases Cohort Studies Colleges & universities Complications and side effects Computer engineering Data processing Diagnosis Electronic health records Enzymes Female Health Health aspects Heart surgery Hospital costs Humans Hypertension Injury analysis Intraoperative Period Kidneys Learning algorithms Machine Learning Male Mathematical models Medicine Medicine and Health Sciences Middle Aged Models, Statistical Morbidity Mortality Nephrology Performance prediction Physiological aspects Physiology Postoperative Complications - etiology Prediction models Predictive Value of Tests Prognosis Reclassification Retrospective Studies Risk Risk Factors Sensitivity and Specificity Statistical analysis Surgery Time series Transplants & implants
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!