This doctoral program aims to develop a scalable multi-disease monitoring service that provides diagnosis support anywhere anytime. The service platform is based on Heart Rate (HR) variations can indicate the presence of diseases like Atrial Fibrillation (AF), myocardial infarction, sleep apnea, and diabetes. The core principle is to fuse Deep Learning (DL) with Internet of Things (IoT) to measure and analyse HR without limitations in terms of patient number and observation duration. We plan to realize the platform by extending the Isansys patient telemetry system with multi-disease diagnosis support functionality. Isansys sensor technology can measure HR in the patient environment and send the signal to a central server in real time. Diagnosis support is established by analysing the signal for symptoms of a specific disease. The DL models process the measurement signals. The DL results are presented with a service specific Graphical User Interface (GUI). DL models and GUIs form distinct modules. The Isansys ecosystem and data access functionality form common modules. The concept of common and distinct modules makes the proposed service platform sufficiently agile to cope with future requirements.
While developing this technology, the doctoral student will engage with healthcare professionals and industrial partners to explore the potential for its commercial deployment. This development is likely to have high impact and value as it enables the multi-disease diagnosis with a single platform. It aligned with both healthcare and market drivers related to e-health. There are five main strengths of the proposed heart health monitoring service platform for multi-disease diagnosis:
1) Positive scalability. The service offerings get cheaper and the diagnosis support quality increases when more patients use them.
2) Reliable and safe diagnosis support through a hybrid decision making process.
3) Real time diagnosis support that improves outcomes for patients.
4) Cost effectiveness through module and resource sharing.
5) Agility to innovate and adopt healthcare services by adding distinct modules for newly identified market segments.
Eligibility
Information on entry requirements can be found at GTA Program Page
How to apply
We strongly recommend you contact the lead academic, Dr. Ningrong Lei, ([Email Address Removed]), to discuss your application
Please visit our GTA program page for more information on the Graduate teaching assistant program and how to apply.
Start date for studentship: October 2021
Interviews are scheduled for: Mid July 2021
For information on how to apply please visit GTA program page
Your application should be emailed to [Email Address Removed] by the closing date of 30th June.
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