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An AI-based monitoring tool for predicting neonatal morbidities: a case study in collaboration with the NHS

   Centre for Digital Innovation

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  Dr Yordanka Karayaneva  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

We are pleased to invite UK, EU and international applications for a fully-funded PhD studentship in An AI-based monitoring tool for predicting neonatal morbidities: a case study in collaboration with the NHS from Teesside University’s Centre for Digital Innovation.

Despite the recent clinical advances in improving preterm infants' outcomes, the incidence of neonatal problems remains still significant. For this reason, more accurate early diagnosis and intervention techniques need to be developed to limit morbidity and mortality in the neonatal population.

In the neonatal intensive care unit (NICU), heart rate, respiratory rate, blood pressure, and oxygen saturation are the vital signs that are usually continuously monitored. Several studies have suggested the importance of using data analytics techniques to analyse these vital signs and predict clinical deterioration or long-term outcomes. However, while some of these data are captured intermittently and used only for time point interventions, the majority of the data is still under-utilised. Specifically, no real-time analysis is performed to identify an early change of trend that could predict impending neonatal problems or long-term outcomes.

Currently, only the variation in heart rate observations is utilised through a very expensive monitor (HeRo, Heart rate observation monitor), which can detect neonatal morbidities (sepsis). The costs of the HeRo monitor are prohibitive which makes this tool not widely utilised yet, and they are only based on babies' heart rate changes, without taking into account any of the other vital signs. HeRo monitors are associated with a high quantity of false positive results.

The aim of this project is to develop a live-monitoring AI tool able to predict the development of morbidities and allow early intervention to serve as a clinical decision support tool to allow clinicians to make informed decisions and limit morbidities/mortalities.

The supervisor is Dr Yordanka Karayaneva from the School of Computing, Engineering & Digital Technologies.

Entry requirements

You should hold or expect to obtain a good honours degree (2:1 or above) in a relevant discipline. A master’s level qualification in a relevant discipline is desirable, but not essential, as well as a demonstrable understanding of the research area.

International applicants should have a standard of English at IELTS 6.5 minimum and will be subject to the standard entry criteria relating to ATAS clearance and, when relevant, UK visa requirements and procedures.

How to apply

Application is online.

Key dates

  • Closing date for applications is 5.00pm, 1 February 2023.
  • Shortlisting and online interviews are expected to be held mid-March 2023.
  • Successful applicants will be expected to start May or October 2023.

Funding Notes

The Fully Funded PhD Studentship covers tuition fees for the period of a full-time PhD Registration of up to four years and provide an annual tax-free stipend of £17,668 for three years, subject to satisfactory progress.
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