IMPROVING HEALTHCARE SYSTEM EFFICIENCY USING MACHINE LEARNING TO PREDICT PATIENT STAY DURATION
DOI:
https://doi.org/10.48047/Keywords:
Healthcare, Machine Learning, KNN, Random Forest, Resource Management, Patient Classification.Abstract
Healthcare demand is rapidly growing both in Australia and globally. In Australia, the healthcare system is a blend of public and private organizations, including hospitals, clinics, and aged care facilities. The system is relatively affordable and accessible, with around 68% of the expenditure funded by the government. In 2015-16, healthcare expenditure amounted to AUD 170.4 billion, representing 10% of
the country's GDP. However, rising healthcare costs and increasing demand for services are straining the sustainability of the government-funded healthcare system. To maintain sustainability, it is essential to improve the efficiency of healthcare service delivery.
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References
Kadri, Farid, Abdelkader Dairi, Fouzi Harrou, and Ying Sun. "Towards accurate prediction of patient length of stay at emergency department: A GAN-driven deep learning framework." Journal of Ambient Intelligence and Humanized Computing 14, no. 9 (2023): 11481-11495.
Zou, Hong, Wei Yang, Meng Wang, Qiao Zhu, Hongyin Liang, Hong Wu, and Lijun Tang. Predicting length of stay ranges by using novel deep neural networks." Heliyon 9, no. 2 (2023).
Saadatmand, Sara, Khodakaram Salimifard, Reza Mohammadi, Alex Kuiper, Maryam Marzban, and Akram Farhadi. "Using machine learning in prediction of ICU admission, mortality, and length of stay in the early stage of admission of COVID-19 patients." Annals of Operations Research 328,
no. 1 (2023): 1043-1071.
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