Mingzhe is currently a PhD student at Cardiff Business School. He has also achieved a Master of Science degree in Logistics and Operations Management from Cardiff University. During his master's studies, Mingzhe attended a live project at Cardiff University called "Anticipating special events in ambulance forecasting."
Several tasks were completed during this live project:
- Create a daily forecast to Health Board level using R.
- Forecasting expected patient demand, including special events forecasting and a recommendation on what forecasting model to use.
Mingzhe is supervised by Dr. Bahman Rostami-Tabar and Professor Daniel Gartner.
The following are links to their respective websites:
My research interests are in applying different time-series forecasting methods in healthcare services, optimization and machine learning applied to unscheduled care services.
Improving the unscheduled planning in urgent and emergency care: Using predictive modeling to deliver quality care
My study seeks to improve the efficiency of unplanned care by identifying sources of uncertainty, building a precise and reliable forecasting framework appropriate for decision-making, and detecting potential crowding characteristics.
We intend to address numerous issues that unplanned care services may be facing at the moment:
- To find different factors that may affect demand and service and incorporate these factors into predictive models;
- To make an accurate prediction of unscheduled care patients flow by using historical time series;
- To use machine learning techniques to transform transactional data into meaningful inputs into a predictive model;
- To link predictive modeling to decisions in Emergency department and Ambulance services, in a pilot hospital such as Bridgend Hospital in Wales, which optimizes decision making process.