Artificial Intelligence and data analytics to improve patient-care outcomes in geriatric population (Advert Reference RDF21/MDRT/IHSC)

This exciting project is a collaborative study within Northumbria University Multidisciplinary Research theme of Integrated Health & Social Care (https://www.northumbria.ac.uk/research/changing-challenging-world/integrated-health-and-social-care/) and Human & Digital Design. The successful applicant will conduct research across the Faculty of Health & Life Sciences and the Department of Computer & Information Sciences (Professor I. Vogiatzis, Professor M. Lhussier and Dr. Li Zhang).

Geriatric clinical care is a multidisciplinary assessment designed to evaluate older patients’ functional ability, physical health, and cognitive well-being. The majority of these patients suffers from multiple chronic conditions and requires special attention. Prediction of exacerbations could diminish those negative effects and reduce the high costs associated with older patients suffering from respiratory and impaired cognitive and mobility conditions and improve quality of life.

The project will explore the performance of daily home recorded respiratory sounds and mobility outcomes for early detection of symptom-based exacerbations by using computerised analysis and artificial intelligence techniques. Respiratory sounds and mobility outcomes will be recorded with electronic sensor ad-hoc designed. In order to enable an automatic prediction of symptom-based exacerbations, recorded data will be used to train and validate a variety of machine learning and deep learning methods.

This will be a sequential mixed study consisting of 4 phases, aligning with the MRC Framework for Complex Interventions aiming to assess the behavioural intention of participants to engage and use the technology at home, and subsequently develop an appropriately tailored vital sign and mobility monitoring intervention alongside appropriate geriatric clinical care pathways for this frail and elderly population. The study phases are the following:

1. Systematic Review on the use of machine learning in geriatric clinical care for chronic diseases.
2. Qualitative Study of service users’ views and experiences of technology use at home environment. This will include patients’ preferences of the nature of the technology and preferred mode of delivery of information.
3. Vision- and audio-based mobility and breathing abnormality detection. This phase aims at delivering:
   i) A deep learning model for mobility abnormality detection using video and/or sensor input
   ii) A deep learning model for breathing abnormality detection using audio/video and/or thermal imaging input
4. System evaluation using existing data sets and real-time inputs:
   i. Develop a comprehensive system evaluation using real-time inputs and existing data sets
   ii. Implement high performance deep learning models for breathing and mobility abnormalities detection

Eligibility and How to Apply:

Home and International (including EU) students are eligible to apply. The studentship includes a full stipend, paid for three years at RCUK rates (for 2020/21, this is £15,285 pa) and full tuition fees.

Please note eligibility requirement:

• Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
• Appropriate IELTS score, if required.
• Applicants cannot apply for this funding if currently engaged in Doctoral study at Northumbria or elsewhere.

For further details of how to apply, entry requirements and the application form, see:

https://www.northumbria.ac.uk/research/postgraduate-research-degrees/how-to-apply/