Abstract
Predicting patients’ length of stay (LOS) is crucial for efficient scheduling of treatment and strategic future planning, in turn reduce hospitalisation costs. However, this is a complex problem requiring careful selection of optimal set of essential factors that significantly impact the accuracy and performance of LOS prediction. Using an inpatient dataset of 285k of records from 14 general care hospitals in Vermont, USA from 2013–2017, we presented our novel approach to incorporate features to improve the accuracy of LOS prediction. Our empirical experiment and analysis showed considerable improvement in LOS prediction with an XGBoost model RMSE score of 6.98 and R2 score of 38.24%. Based on several experiments, we provided empirical analysis of the importance of different feature sets and its impact on predicting patients’ LOS.
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Acknowledgements
We would like to thank the State of Vermont for providing us with the Vermont Uniform Hospital Discharge Dataset in agreement with its Public Use policy. We also thank Jirong Liu and Young Rang Choi for helping out with the visual data analysis needed for this work.
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Osop, H., Suleiman, B., Alibasa, M.J., Wrigley, D., Helsham, A., Asmaro, A. (2023). Improving Patients’ Length of Stay Prediction Using Clinical and Demographics Features Enrichment. In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 14074. Springer, Cham. https://doi.org/10.1007/978-3-031-36021-3_9
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