A Classification and Regression Tree Analysis for Prediction of Surgical Patient Lengths of Stay
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Abstract
Healthcare demand is growing in Australia and across the world. In Australia, the healthcare system comprises a mix of private and public organizations, such as hospitals, clinics, and aged care facilities. The Australian healthcare system is quite affordable and accessible because a large proportion of the expenditure, around 68%, is funded by the Australian government. The healthcare expenditure in 2015-16 was AUD 170.4 billion which was 10.0% of the GDP. Soaring healthcare costs and growing demand for services are increasing the pressure on the sustainability of the government-funded healthcare system. To be sustainable, we need to be more efficient in delivering healthcare services. We can schedule the care delivery process optimally and subsequently improve the efficiency of the system if demand for services is well known. However, there is a randomness in demand for services, and it is a cause of inefficiency in the healthcare delivery process. Soaring healthcare costs and the growing demand for services require us to use healthcare resources more efficiently. Randomness in resource requirements makes the care delivery process less efficient. Our aim is to reduce the uncertainty in patients’ resource requirements, and we achieve that objective by classifying patients into similar resource user groups. The conventional random forest., k-nearest neighbourhood (KNN) methods were resulted in poor classification, prediction performance. In this work, we develop a two-stage classification model to classify patients into lower variability resource user groups by using electronic patient record. There are various statistical tools for classifying patients into lower variability resource user groups. However, classification and regression tree (CART) analysis is a more suitable method for analyzing healthcare data because it has some distinct features. For example, it can handle the interaction between predictor variables naturally, it is nonparametric in nature, and it is relatively insensitive to the curse of dimensionality.
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References
AIHW, “Australia’s health series no. 15. Cat. no. AUS 199,” Canberra: AIHW, 2015, ”[Online;
accessed 25-September-2022]”. [Online]. Available: https://www.aihw.gov.au/reports/australiashealth/ australias-health-2016/contents/summary
P. R. Harper, “A framework for operational modelling of hospital resources,” Health Care Manag. Sci.,
vol. 5, no. 3, pp. 165–173, 2002.
R. McMullan, B. Silke, K. Bennett and S.Callachand. “Resource utilisation, length of hospital stay,
and pattern of investigation during acute medical hospital admission”. Postgrad Med J. 2004
Jan;80(939):23-6. doi: 10.1136/pmj.2003.007500. PMID: 14760174; PMCID: PMC1757957.
M. Faddy, N. Graves, and A. Pettitt, “Modeling length of stay in hospital and other right skewed data:
Comparison of phase-type, gamma and lognormal distributions”, Value Heal., vol. 12, no. 2, pp. 309–
, 2009
V. Liu, P. Kipnis, M. K. Gould and G. J. Escobar. “Length of stay predictions: improvements through
the use of automated laboratory and comorbidity variables”. Med Care. 2010;48(8):739–44.
https://doi.org/10.1097/MLR.0b013e3181e359f3
X. Jiang, X. Qu and L. Davis. “Using Data Mining to Analyze Patient Discharge Data for an Urban
Hospital”. In: DMIN; 2010. p. 139–144.
A. Freitas, T. Silva-Costa and F. Lopes. “Factors influencing hospital high length of stay outliers”.
BMC Health Serv Res 12, 265 (2012). https://doi.org/10.1186/1472-6963-12-265
E. M. Carter and H. W. Potts. “Predicting length of stay from an electronic patient record system: a
primary total knee replacement example”. BMC Med Inform Decis Mak 14, 26 (2014).
https://doi.org/10.1186/1472-6947-14-26
A. Morton, E. Marzban, G. Giannoulis, A. Patel, R. Aparasu and I. A. Kakadiaris, “A Comparison of
Supervised Machine Learning Techniques for Predicting Short-Term In-Hospital Length of Stay
among Diabetic Patients”, 2014 13th International Conference on Machine Learning and Applications,
, pp. 428-431, doi: 10.1109/ICMLA.2014.76.
S. Sushmita, S. Newman and J. Marquardt. “Population cost prediction on public healthcare datasets”.
In: Proceedings of the 5th international conference on digital health 2015. ACM; 2015. Pp. 87–94.
https://doi.org/10.1145/2750511.2750521
A. R. Al Taleb, M. Hoque, A. Hasanat and M. B. Khan, “Application of data mining techniques to
predict length of stay of stroke patients”, 2017 International Conference on Informatics, Health &
Technology (ICIHT), 2017, pp. 1-5, doi: 10.1109/ICIHT.2017.7899004.
V. A. Smith, B. Neelon and M. L. Maciejewski. “Two parts are better than one: modeling marginal
means of semicontinuous data”, Health Serv Outcomes Res Method 17, 198–218 (2017).
https://doi.org/10.1007/s10742-017-0169-9
Ting Zhu, Li Luo, Xinli Zhang, Yingkang Shi and Wenwu Shen. “Time-Series Approaches for
Forecasting the Number of Hospital Daily Discharged Inpatients”, IEEE J Biomed Health Inform.
Mar;21(2):515-526. doi: 10.1109/JBHI.2015.2511820. Epub 2015 Dec 23. PMID: 28055928.
M. Rouzbahman, A. Jovicic and M. Chignell, “Can Cluster-Boosted Regression Improve Prediction
of Death and Length of Stay in the ICU?”, in IEEE Journal of Biomedical and Health Informatics, vol.
, no. 3, pp. 851-858, May 2017, doi: 10.1109/JBHI.2016.2525731.
I. E. Livieris, T. Kotsilieris, I. Dimopoulos and P. Pintelas. “Decision Support Software for Forecasting
Patient’s Length of Stay”, Algorithms 2018, 11, 199. https://doi.org/10.3390/a11120199