Predicting daily outcomes in acetaminophen-induced acute liver failure patients with machine learning techniques.

Journal: Computer methods and programs in biomedicine
PMID: 31104700

Abstract

BACKGROUND/OBJECTIVE: Assessing prognosis for acetaminophen-induced acute liver failure (APAP-ALF) patients during the first week of hospitalization often presents significant challenges. Current models such as the King's College Criteria (KCC) and the Acute Liver Failure Study Group (ALFSG) Prognostic Index are developed to predict outcome using only a single time point on hospital admission. Models using longitudinal data are not currently available for APAP-ALF patients. We aim to develop and compare performance of prediction models for outcomes during the first week of hospitalization for APAP-ALF patients.

Authors

  • Jaime Lynn Speiser
    Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA.
  • Constantine J Karvellas
    Divisions of Hepatology and Critical Care Medicine, University of Alberta, Edmonton, Canada.
  • Bethany J Wolf
    Dept. of Public Health Sciences, Medical University of South Carolina, USA.
  • Dongjun Chung
    Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, U.S.A.
  • David G Koch
    Division of Gastroenterology and Hepatology, Department of Medicine, Medical University of South Carolina, Charleston, SC, United States.
  • Valerie L Durkalski
    Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, United States.

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