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Predicting overcrowding in the acute care domain using random forest regression

Tokarczyk, F. (2020) Predicting overcrowding in the acute care domain using random forest regression.

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Abstract:Crowding is a phenomenon that occurs more frequently within the facilities of the acute care domain. Acute care providers are interested in; means to quantify crowding, potential predictors of crowding, and a model to forecast crowding. This information can help the facilities to plan accordingly to future acute care demand. In this research, we focus on these topics and provide a machine learning model for an emergency department and general practitioner's post in Gelderland. This model predicts the total daily visitors to the facilities based on a variety of external variables, such as weather data, pollen data, et cetera and provides an overview of the most important variables in the models.
Item Type:Essay (Master)
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:50 technical science in general, 54 computer science, 85 business administration, organizational science
Programme:Industrial Engineering and Management MSc (60029)
Link to this item:http://purl.utwente.nl/essays/85274
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