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

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.

Materials and methods

We utilized electronic health record data from 34,575 patients with a diagnosis of cirrhosis from a large medical center to study associations with mortality. Three time-windows of mortality (365 days, 180 days and 90 days) and two

SUBMITTER: Guo A 

PROVIDER: S-EPMC8407576 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature