Applying Supervised Machine Learning to Identify Which Patient Characteristics Identify the Highest Rates of Mortality Post-Interhospital Transfer.

ABSTRACT:

Objective

To demonstrate the usefulness of applying supervised machine-learning analyses to identify specific groups of patients that experience high levels of mortality post-interhospital transfer.

Methods

This was a cross-sectional analysis of data from the Health Care Utilization Project 2013 National Inpatient Sample, that applied supervised machine-learning approaches that included (1) classification and regression tree to identify mutually exclusive groups of patients and their associated characteristics of those experiencing the highest levels of mortality and (2) random forest to identify the relative importance of each characteristic's contribution to post-transfer mortality.

Results

A total of 21 independent groups of patients were identified, with 13 of tho

SUBMITTER: Reimer AP 

PROVIDER: S-EPMC6425528 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature