A Classification and Regression Tree Analysis for Prediction of Surgical Patient Lengths of Stay
Keywords:
Classification and Regression tree, Length of stay patient, KNN.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.
Downloads
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/australias-health/ australias-health-
/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. 309–314, 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 InHospital Length of Stay among Diabetic Patients”, 2014 13th International Conference on
Machine Learning and Applications, 2014, 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
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:
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,
–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. 2017 Mar;21(2):515-526. doi: 10.1109/JBHI.2015.2511820. Epub
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. 21, 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.
Downloads
Published
Issue
Section
License
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
