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Forecasting weekly emergency department demand in a Portuguese private hospital - generalising to a medium-sized unit
| Nome: | Descrição: | Tamanho: | Formato: | |
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| 12.15 MB | Adobe PDF |
Autores
Orientador(es)
Resumo(s)
The international Emergency Department (ED) overcrowding crisis affects both private and public
Portuguese hospitals, which can be mitigated by an efficient medium-term operational planning. In
this light, a Machine Learning multi-step-ahead predictive tool to forecast weekly ED arrivals in the
largest unit of a private Portuguese healthcare provider, CUF, was developed. Linear Regression,
SARIMAX and LSTM were evaluated and compared. SARIMAX, which obtained the best results,
proved to have adequate predictive accuracy to support ED management. Additionally, the question
of whether this model could be generalised to a medium-sized CUF ED unit was studied.
Keywords: Healthcare, Emergency Department, Machine Learning, Time Series, Multi-step-ahead
Forecasting, Model Generalisation.
Descrição
Palavras-chave
Healthcare Emergency department Machine learning Time series Multi-step-ahead forecasting