Predictive analytics for the early detection of exacerbation of disease

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Telehealth monitoring for the management of chronic disease is becoming increasingly routine internationally and shows great promise in reducing health service costs through the reduction of unnecessary hospital admissions. Scaling up telehealth services nationally represents a complex organisational and technical challenge that will require automated means of assessing changes in an individual health status. This project seeks to develop robust statistical models of an individual’s health status from longitudinal measurements of their vital signs and questionnaire data taken at home, with the objective of identifying exacerbations and orchestrating an optimal response from health services to reduce unscheduled admissions to hospital

Honours degree in Biomedical Engineering, Electrical Engineering and Computer engineering. A good background in mathematics and statistics and an interest in medical informatics. Alternatively a medical graduate interested in pursuing a career in medical informatics and decision support.

Supervisory team

Electrical Engineering and Telecommunications

Public Health & Community Medicine

Electrical Engineering and Telecommunications
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