Abstract
The primary objective of hospital managers is to establish appropriate healthcare planning and organisation by allocating facilities, equipment and manpower resources necessary for hospital operation in accordance with a patients needs while minimising the cost of healthcare. Length of stay (LoS) prediction is generally regarded as an important measure of inpatient hospitalisation costs and resource utilisation. LoS prediction is critical to ensuring that patients receive the best possible level of care during their stay in hospital. A novel approach for the prediction of LoS is investigated in this paper using only data based upon generic patient diagnoses. This data has been collected during a patients stay in hospital along with other general personal information such as age, sex, etc. A number of different classifiers are employed in order to gain an understanding of the ability to perform knowledge discovery on this limited dataset. They demonstrate a classification accuracy of around 75%. In addition, a further set of perspectives are explored that offer a unique insight into the contribution of the individual features and how the conclusions might be used to influence decision-making, staff and resource scheduling and management.
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Acknowledgements
Kieran Stone would like to acknowledge the financial support for this research through Knowledge Economy Skills Scholarship (KESS 2). It is part funded by the Welsh Government’s European Social Fund (ESF) convergence programme for West Wales and the Valleys. WEFO (Welsh European Funding Office) contract number: C80815.
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Stone, K., Zwiggelaar, R., Jones, P., Parthaláin, N.M. (2020). Predicting Hospital Length of Stay for Accident and Emergency Admissions. In: Ju, Z., Yang, L., Yang, C., Gegov, A., Zhou, D. (eds) Advances in Computational Intelligence Systems. UKCI 2019. Advances in Intelligent Systems and Computing, vol 1043. Springer, Cham. https://doi.org/10.1007/978-3-030-29933-0_24
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DOI: https://doi.org/10.1007/978-3-030-29933-0_24
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