Journal
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
Volume 32, Issue 10, Pages 2849-2859Publisher
SPRINGER
DOI: 10.1007/s00477-018-1519-z
Keywords
Forecasting; Emergency hospital admissions; ARIMA; Neural networks; Random forests; Gradient boosting machines; Stacked generalization
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Funding
- Ministerio de Economia y Competitividad, Gobierno de Espana through a Ramon y Cajal Grant [RYC-2012-11984]
- Instituto Mixto de Investigacion ENS-UNED (IMIENS)
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Anticipating future workloads in a hospital may be of capital importance in order to distribute resources and improve patient attention. In this paper, we tackle the problem of predicting daily hospital admissions in Madrid due to circulatory and respiratory cases based on biometeorological indicators. A range of forecasting algorithms were proposed covering four model families: ensemble methods, boosting methods, artificial neural networks and ARIMA. Experiments show how the last two obtain better results in average, demonstrating that the problem can be properly solved with both approaches. Furthermore, a recently proposed technique known as stacked generalization was also used to dynamically combine the predictions from the four models, finally improving the performance with respect to the individual models.
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