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BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2019

Open Access 01-12-2019 | Research article

Recursive neural networks in hospital bed occupancy forecasting

Published in: BMC Medical Informatics and Decision Making | Issue 1/2019

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Abstract

Background

Efficient planning of hospital bed usage is a necessary condition to minimize the hospital costs. In the presented work we deal with the problem of occupancy forecasting in the scale of several months, with a focus on personnel’s holiday planning.

Methods

We construct a model based on a set of recursive neural networks, which performs an occupancy prediction using historical admission and release data combined with external factors such as public and school holidays. The model requires no personal information on patients or staff. It is optimized for a 60 days forecast during the summer season (May–September).

Results

An average mean absolute percentage error (MAPE) of 6.24% was computed on 8 validation sets.

Conclusions

The proposed machine learning model has shown to be competitive to standard time-series forecasting models and can be recommended for incorporation in medium-size hospitals automatized scheduling and decision making.
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Metadata
Title
Recursive neural networks in hospital bed occupancy forecasting
Publication date
01-12-2019
Published in
BMC Medical Informatics and Decision Making / Issue 1/2019
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-019-0776-1

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