Predicting mortality among patients with liver cirrhosis in electronic health records with machine learning.

Journal: PloS one
Published Date:

Abstract

OBJECTIVE: Liver cirrhosis is a leading cause of death and effects millions of people in the United States. Early mortality prediction among patients with cirrhosis might give healthcare providers more opportunity to effectively treat the condition. We hypothesized that laboratory test results and other related diagnoses would be associated with mortality in this population. Our another assumption was that a deep learning model could outperform the current Model for End Stage Liver disease (MELD) score in predicting mortality.

Authors

  • Aixia Guo
    Institute for Informatics (I2), Washington University School of Medicine, St. Louis, MO, United States.
  • Nikhilesh R Mazumder
    Division of Gastroenterology, Northwestern Memorial Hospital, Chicago, IL, United States of America.
  • Daniela P Ladner
    Northwestern University Transplant Outcomes Research Collaborative (NUTORC), Comprehensive Transplant Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States of America.
  • Randi E Foraker
    Institute for Informatics (I2), Washington University School of Medicine, St. Louis, MO, United States.