Skip to Main Content

Paper Details

Development of an automated phenotyping algorithm for hepatorenal syndrome.
J Biomed Inform
23
2018
AKI, EHR, HRS, Hepatorenal Syndrome, Kidney Disease, acute illness, acute kidney injury, advanced liver disease, cirrhosis, hepatorenal syndrome, patients, predictor variables, predictors, structured
Acute Kidney Injury, Aged, Algorithms, Diagnosis, Computer-Assisted, Electronic Health Records, Female, Hepatorenal Syndrome, Humans, Liver Cirrhosis, Male, Middle Aged, Natural Language Processing, Odds Ratio, Phenotype, ROC Curve, Retrospective Studies, Support Vector Machine
Author NameAffiliation
Jejo KoolaGeriatric Research Education and Clinical Center (GRECC), Tennessee Valley Healthcare System Veterans Administration Medical Center, University of California, USA Division of Hospital Medicine
Sharon E DavisGeriatric Research Education and Clinical Center (GRECC), Tennessee Valley Healthcare System Veterans Administration Medical Center, Vanderbilt University Medical Center
Omar NimriNorthwest Renal Clinic
Sharidan K ParrGeriatric Research Education and Clinical Center (GRECC), Tennessee Valley Healthcare System Veterans Administration Medical Center, Vanderbilt University Medical Center
Daniel FabbriVanderbilt University Medical Center, Vanderbilt University
Bradley A MalinVanderbilt University Medical Center, Vanderbilt University
Samuel B HoVA San Diego Healthcare System, University of California
Michael MathenyGeriatric Research Education and Clinical Center (GRECC), Tennessee Valley Healthcare System Veterans Administration Medical Center, Vanderbilt University Medical Center
Michael E MathenyGeriatric Research Education and Clinical Center (GRECC), Tennessee Valley Healthcare System Veterans Administration Medical Center, Vanderbilt University Medical Center
  • 1 - 9

Datasets

Release 1.2