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Forecasting patient demand and predicting inpatient admission via machine learning techniques in acute care domain

Ibrahim, Arief (2019) Forecasting patient demand and predicting inpatient admission via machine learning techniques in acute care domain.

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Abstract:In this thesis, the Emergency Department (ED) and the general Practitioner Post (GP-Post) at Winterswijk in the Netherlands are selected as a case study to research and develop forecasting tools based on the internal historical data and also external data such as weather and pollen data. This forecasting tool is expected to function as an early warning alarm that can properly anticipate the overcrowding events at ED and GP-Post of Winterswijk. Besides, the stakeholders are interested in predicting the probability of inpatient admission to the hospital with classification methods in machine learning. Having an automate classifier tool can help the management to assess the quality of their service and operation and parallelly improve it as well. Apart from the two machine learning techniques, stakeholders also have high curiosity in analyzing a linear correlation between external factors (e.g., weather and pollen) with some particular patient groups (e.g., age groups, treatment groups) during a certain period such as weekdays-weekends or seasons. With these additional insights on hands, the management, practitioners, or even staffs at ED and GP-Post might have a better understanding of treating the patients in a specific situation (e.g., extreme heat temperature).
Item Type:Essay (Master)
Clients:
Acute Zorg Euregio, Enschede, Netherlands
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Business Information Technology MSc (60025)
Link to this item:https://purl.utwente.nl/essays/79016
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