Training and Interpreting Machine Learning Algorithms to Evaluate Fall Risk After Emergency Department Visits.

ABSTRACT:

Background

Machine learning is increasingly used for risk stratification in health care. Achieving accurate predictive models do not improve outcomes if they cannot be translated into efficacious intervention. Here we examine the potential utility of automated risk stratification and referral intervention to screen older adults for fall risk after emergency department (ED) visits.

Objective

This study evaluated several machine learning methodologies for the creation of a risk stratification algorithm using electronic health record data and estimated the effects of a resultant intervention based on algorithm performance in test data.

Methods

Data available at the time of ED discharge were retrospectively collected and separated into training and test datasets. Algorith

SUBMITTER: Patterson BW 

PROVIDER: S-EPMC6590914 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature