GBOLAHAN, Aramide (2017). Big data analytics: reducing unplanned admission in Nigeria. In: Computational Intelligence for Societal Development in Developing Countries (CISDIDC), Sheffield Hallam University, 17 February 2017. (Unpublished) [Conference or Workshop Item]
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Gbolahan CISDIDC2017.pdf - Accepted Version
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Gbolahan CISDIDC2017.pdf - Accepted Version
Available under License All rights reserved.
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Abstract
Unplanned hospital admissions are unpredictable admission at short notice, which are often presented at the Accident and Emergency department. They mostly occur when a patient is admitted at earliest possible time, with an overnight stay on short notice, due to patients’ clinical requirement or an alternate healthcare service. Literature on unplanned admission in Nigerian hospitals has not been well studied, which is why this presentation would give detailed exploration of a conjectural data, in order to identify factors predisposing patients to hospital admission in Nigeria, using data mining techniques. The outcome of this presentation would not only give insight to ways of improving the healthcare system in Nigeria but a detail understanding on how the health authorities can adequately manage identified factors, in order to mitigate emergency admission in Nigerian Hospitals.
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