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Paper Details

Calibration drift in regression and machine learning models for acute kidney injury.
J Am Med Inform Assoc
136
2017
acute kidney injury, hospital, patient
Acute Kidney Injury, Aged, Bayes Theorem, Clinical Decision-Making, Decision Support Techniques, Female, Hospitals, Veterans, Humans, Logistic Models, Machine Learning, Male, Middle Aged, United States
Author NameAffiliation
Sharon E DavisVanderbilt University School of Medicine
Thomas A LaskoVanderbilt University School of Medicine
Guanhua ChenVanderbilt University School of Medicine.
Guanhua ChenVanderbilt University School of Medicine.
Edward D SiewGeriatric Research Education and Clinical Care Service, VA Tennessee Valley Healthcare System
Edward D SiewVanderbilt University School of Medicine, Vanderbilt Center for Kidney Disease and Integrated Program for AKI
Michael MathenyVanderbilt University School of Medicine
Michael MathenyVanderbilt University School of Medicine.
Michael MathenyGeriatric Research Education and Clinical Care Service, VA Tennessee Valley Healthcare System
Michael MathenyVanderbilt University School of Medicine.
Michael E MathenyVanderbilt University School of Medicine
Michael E MathenyVanderbilt University School of Medicine.
Michael E MathenyGeriatric Research Education and Clinical Care Service, VA Tennessee Valley Healthcare System
Michael E MathenyVanderbilt University School of Medicine.
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Release 1.2