Recursive neural networks in hospital bed occupancy forecasting.

Journal: BMC medical informatics and decision making

Published Date: Mar 7, 2019

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.

Authors

Ekaterina Kutafina

Department of Medical Informatics, Uniklinik RWTH Aachen, Aachen, Germany.
Istvan Bechtold

St. Marien- und St. Annastiftskrankenhaus, Salzburger Straße 15, 67067, Ludwigshafen, Germany.
Klaus Kabino

Department of Medical Informatics, Uniklinik RWTH Aachen, Pauwelsstrasse 30, 52057, Aachen, Germany.
Stephan M Jonas

Department of Medical Informatics, Uniklinik RWTH Aachen, Aachen, Germany.

Keywords

Bed Occupancy Forecasting Holidays Hospitals Humans Machine Learning Models, Theoretical Neural Networks, Computer

External Resources

View on PubMed Access via DOI PubMed (30845940)

Recursive neural networks in hospital bed occupancy forecasting.

Abstract

Authors

Keywords

External Resources

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