Higher Degree Research Projects
Predictive Analytics Framework For Electronic Health Records with Machine Learning Advancements: Optimising Hospital Resources Utilisation with Predictive and Epidemiological Models
Resources utilisation in hospitals is vital for hospital healthcare management systems. Managing hospital beds’ availability and efficiency is essential for addressing challenges associated with the overabundance of patients in ICU and hospital. This project has developed a research framework and examined the feasibility and robustness of predictive machine-learning models in the context of improving hospital resources’ utilisation with data-driven approaches and predicting hospitalisation with hospital quality assessment metrics such as length of stay. While the hospital length of stay predictions are (internal) healthcare inpatients outcomes assessment at the time of admission to discharge, the project also considered (external) factors outside hospital control, such as forecasting future hospitalisations from the spread of infectious communicable disease during pandemics using epidemiological models. The findings support the usefulness of prediction in the influx of patients during pandemics and how machine learning predictive models can assist healthcare workers and improve hospital resources utilisation from a data-driven approach.
Developed By:
Belal Alsinglawi