Assessment of Time-Series Machine Learning Methods for Forecasting Hospital Discharge Volume.

ABSTRACT:

Importance

Forecasting the volume of hospital discharges has important implications for resource allocation and represents an opportunity to improve patient safety at periods of elevated risk.

Objective

To determine the performance of a new time-series machine learning method for forecasting hospital discharge volume compared with simpler methods.

Design

A retrospective cohort study of daily hospital discharge volumes at 2 large, New England academic medical centers between January 1, 2005, and December 31, 2014 (hospital 1), or January 1, 2005, and December 31, 2010 (hospital 2), comparing time-series forecasting methods for prediction was performed. Data analysis was conducted from February 28, 2017, to August 30, 2018. Group-level data for all discharges from inpat

SUBMITTER: McCoy TH 

PROVIDER: S-EPMC6324591 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature