Big data analytics: reducing unplanned admission in Nigeria

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 (Unpublished)

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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.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	Editors of book of abstracts: Andy Dearden, Kennedy Ehimwenma, Aramide Gbolahon, Raj Ramachandran.
Depositing User:	Helen Garner
Date Deposited:	24 May 2017 10:23
Last Modified:	18 Mar 2021 16:07
URI:	https://shura.shu.ac.uk/id/eprint/15772

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