Related references
Note: Only part of the references are listed.Artificial intelligence and machine learning for predicting acute kidney injury in severely burned patients: A proof of concept
Nam K. Tran et al.
BURNS (2019)
A clinically applicable approach to continuous prediction of future acute kidney injury
Nenad Tomasev et al.
NATURE (2019)
Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods
Hooman H. Rashidi et al.
ACADEMIC PATHOLOGY (2019)
Machine learning versus physicians' prediction of acute kidney injury in critically ill adults: a prospective evaluation of the AKIpredictor
Marine Flechet et al.
CRITICAL CARE (2019)
Adverse Event Reporting in Clinical Trials: Time to Include Duration as Well as Severity
Oliver Sartor
ONCOLOGIST (2018)
Prevalence and risk factors for acute kidney injury among trauma patients: a multicenter cohort study
Anatole Harrois et al.
CRITICAL CARE (2018)
Calibration drift in regression and machine learning models for acute kidney injury
Sharon E. Davis et al.
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION (2017)
Acute kidney injury in trauma patients
Anatole Harrois et al.
CURRENT OPINION IN CRITICAL CARE (2017)
Sepsis in the burn patient: a different problem than sepsis in the general population
David G. Greenhalgh
BURNS & TRAUMA (2017)
The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms
Spyros Makridakis
FUTURES (2017)
Prediction and detection models for acute kidney injury in hospitalized older adults
Rohit J. Kate et al.
BMC MEDICAL INFORMATICS AND DECISION MAKING (2016)
Early Predictors of Acute Kidney Injury: A Narrative Review
Xiaoqin Liu et al.
KIDNEY & BLOOD PRESSURE RESEARCH (2016)
The metabolic stress response to burn trauma: current understanding and therapies
Craig Porter et al.
LANCET (2016)
Introduction to machine learning: k-nearest neighbors
Zhongheng Zhang
ANNALS OF TRANSLATIONAL MEDICINE (2016)
Point-of-Care B-Type Natriuretic Peptide and Neutrophil Gelatinase-Associated Lipocalin Measurements for Acute Resuscitation: A Pilot Study
Erin Howell et al.
JOURNAL OF BURN CARE & RESEARCH (2015)
Whole blood neutrophil gelatinase-associated lipocalin predicts acute kidney injury in burn patients
Soman Sen et al.
JOURNAL OF SURGICAL RESEARCH (2015)
Cystatin C, NT-proBNP, and inflammatory markers in acute heart failure: insights into the cardiorenal syndrome
J. P. E. Lassus et al.
BIOMARKERS (2011)
Neutrophil gelatinase-associated lipocalin (NGAL) as a Biomarker for Early Acute Kidney Injury
Douglas Shemin et al.
CRITICAL CARE CLINICS (2011)
Understanding urine output in critically ill patients
Matthieu Legrand et al.
ANNALS OF INTENSIVE CARE (2011)
Acute kidney injury in critically ill burn patients. Risk factors, progression and impact on mortality
Tina Palmieri et al.
BURNS (2010)
An assessment of acute kidney injury with modified RIFLE criteria in pediatric patients with severe burns
Tina Palmieri et al.
INTENSIVE CARE MEDICINE (2009)
Biological variation of cystatin C and creatinine
Mark Reinhard et al.
SCANDINAVIAN JOURNAL OF CLINICAL & LABORATORY INVESTIGATION (2009)