Past opportunities

Multimorbidity PhD Programme for Health Professionals

Topic: Co-existing mental and physical multimorbidity, adverse events, and longer term outcomes in hospitalised patients with sepsis

The Clinical PhD Research Fellowships are fixed term 3-year appointments offering training, mentoring and support to health professionals undertaking a PhD on the topic of multimorbidity.

Summary:

Sepsis is a common, life-threatening condition, causing over 45,000 deaths annually. Sepsis survivors commonly experience new physical and mental health problems. Multimorbidity is common in patients who develop sepsis, of whom 20% of critically ill patients have mental health comorbidity and is associated with higher mortality and post-discharge rehospitalisation. However, the impact of co-existing mental and physical multimorbidity in the context of sepsis is unclear. In particular, its impact on adverse events, outcomes and recovery has not been previously investigated.

The overall aim of the studentship is to evaluate the impact of co-existing mental and physical multimorbidity on in-hospital adverse events and outcomes, and longer-term recovery, for hospitalised patients with sepsis in order to inform improvements in care quality.  

During the studentship, epidemiological analyses will be undertaken to determine associations between co-existing mental illness/physical multimorbidity and both acute outcomes and post-discharge recovery, underpinned by explicit causal frameworks. Subsequently, clusters of mental/physical multimorbidity will be examined using artificial intelligence methods.

The studentship will provide training in epidemiology, causal inference, and machine learning methods. The student will benefit from the vibrant, academic environment in the Usher Institute and synergistic learning from other multimorbidity work undertaken by the team (https://edin.ac/3CqoERz).

Supervisors:

Dr Nazir Lone, Usher Institute (University of Edinburgh)

Prof Jacques Fleuriot, School of Informatics (University of Edinburgh)

Prof Manu Shankar-Hari, Centre for Inflammation Research (University of Edinburgh)

Closing date: 19th September 2022

Further information and the application procedure are available here.

Research Fellow in Knowledge Representation for Medical Artificial Intelligence

We have an open position for a Research Fellow in Knowledge Representation for Medical Artificial Intelligence

Salary: £34,304-£40,927
Closing date: 9th August 2022 (17:00, UK time)

Informal enquiries may be directed to Bruce Guthrie, Professor of General Practice at the University of Edinburgh (Bruce.Guthrie@ed.ac.uk) or Jacques Fleuriot, Director of the Artificial Intelligence and its Applications Institute, School of Informatics (Jacques.Fleuriot@ed.ac.uk)

Further information

We are looking for a AI researcher with experience in knowledge representation/modelling and reasoning to join our inter-disciplinary programme of research (AIM-CISC), which focuses on the application of advanced AI methods to better understand and address the increasing challenge posed to healthcare systems by multimorbidity (i.e. multiple chronic conditions in people).

The application procedure is available here.

Research Associate in Machine Learning for Health and Medicine

Applications are invited for a Postdoctoral Research Associate in machine learning for health and medicine in the School of Informatics, University of Edinburgh.

 

We are looking for an exceptional researcher to apply AI/machine learning techniques to the analysis and prediction of health-related, adverse events for people with complex, long-term conditions (multimorbidity) and polypharmacy.

Experience in machine learning for graph-structured data is highly desirable.

This full-time position is fixed term till the 31st of July 2024.

Informal enquiries to be directed to Jacques Fleuriot
(jdf@inf.ed.ac.uk).

Salary grade scale: UE07 (£34,304 – £40,927pa)

Feedback is only provided to interviewed candidates.

Closing date 5pm (GMT) on 25th April 2022.

Further information

Artificial Intelligence and Multimorbidity: Clustering in Individuals, Space and Clinical Context (AIM-CISC) is a 3 year, £3.9 million research programme funded by the National Institute for Health Research (NIHR) Artificial Intelligence for Multiple Long-Term Conditions Programme. AIM-CISC is led by a multidisciplinary team from The University of Edinburgh, University College London, and NHS Lothian.

Further information and the application procedure are available here.

Research Associate in Formal Modelling for Health and Care

Applications are invited for a Research Associate in Formal Modelling for Health and Care in the School of Informatics, University of Edinburgh.

This post will contribute to the New Technologies of Care Programme within the Advanced Care Research Centre (ACRC) at the University of Edinburgh.

This full-time position is fixed term for 30 months.

Informal enquiries to be directed to Jacques Fleuriot
(jdf@inf.ed.ac.uk), Petros Papapanagiotou (pe.p@ed.ac.uk) and Jane
Hillston (Jane.Hillston@ed.ac.uk).
 

Salary grade scale: UE07 (£34,304 – £40,927pa)

Feedback is only provided to interviewed candidates.

Closing date 5pm (GMT) on 24th January 2022.

Further information

To support meaningful inference and decision making in the context of advanced care delivery for the older person, computer-based models are needed that can capture the vast body of
knowledge both around general and specific care pathways and incorporate
data from real-time sources such as sensors. In the case of the person
in later life, there is a need for contextualisation with respect to
both the care recipient and their environment.

One objective of this project is to build computer-based models that enable both the management and analysis of care,
while supporting decision making. Process-based models can help map
complex sequences of events, such as routines of daily living or care
plans and pathways. Models will be constructed and parameterised based
on sensor, video and recorded care data (coming from other streams of
the New Technologies of Care Programme and the broader ACRC). AI-based workflow analytics and other techniques will be applied to the models to understand the context of and predict adverse events and health outcomes.

Bridging the gap between the high complexity of the care landscape, the individual needs and circumstances of the older person, and the development of computer-based workflows that interface between data and processes in order to drive decision-making will be one of the important goals of this multidisciplinary project.

Further information and the application procedure are available here.

Fully Funded PhD: Monitoring daily living to predict health outcomes

Supervisors: Petros Papapanagiotou (principal), Atul Anand, Phillip Whitehead (Newcastle), John Vines

Funding: Advanced Care Research Centre (ACRC).

Deadline: 26 November 2021
More info and links

Aim

The aim is to build a data- and process-driven, long-term predictive model of health outcomes based on the monitoring of Activities of Daily Living (ADLs) of the target older population.

Objectives 

  • Data curation for ADLs working with actual users 
  • Investigation of links of ADL monitoring data to health indicators based on medical and empirical evidence 
  • Development of combined data- and process-based AI techniques for long-term prediction of deterioration 

Description

The Internet of Things and modern smart homes offer us unprecedented insights in our daily activities and the means to improve and automate them. This project aims to exploit the available sensor data to help detect and predict health deterioration in older people. This involves collecting and curating real data, from sleeping and eating habits, to the speed of climbing the stairs. You will then employ and develop advanced process modelling techniques to capture daily routines and data-driven AI to predict long-term changes. The project requires a multi-disciplinary approach that incorporates relevant medical information and ethical considerations. 

The project will be part of the ACRC theme on New Technologies of Care and is aligned with other themes such as the one on understanding the person in context.

The PhD position is fully funded by a studentship and the deadline for applications is the 26 November 2021. Informal inquiries can be sent to Petros Papapanagiotou (pe.p@ed.ac.uk).

More information and application details can be found here.

Fully Funded PhD: Building explainable user models of older adults from data

Supervisors:  Jacques Fleuriot (principal), Sohan Seth & Susan Shenkin

Funding: Advanced Care Research Centre (ACRC).

Deadline: 5 May 2021

As artificial intelligence increasingly permeates all spheres of life, it is becoming clear that there is a need for predictive models that can explain their decisions. This is particularly important in safety-critical areas such as health and care, where the wrong decision can be a matter of life or death.

This project will explore how health and care outcomes for the older person can be improved through explainable, predictive machine learning. In particular, it will develop interpretable AI models of older adults, based on a combination of statistical and symbolic approaches using data related to care, physiological monitoring, activities of daily living and other events (e.g. social network interactions).

By developing robust, yet adaptive and transparent, user models that can support the individual’s needs and are attentive to physical/non-physical decline over time, it should be possible to increase the reliance on AI when making non-trivial care interventions.

Some of the objectives include:

  • Exploring how already-labelled data about care treatment, alarm calls, history of falls etc. can be used to detect adverse events and trigger personalised alerts that are robust to noise;
  • Investigate whether decline can be predicted based on reduced interactions with entertainment systems, games, frequency of audio/video chats family and friends, etc.;
  • Investigate how public datasets can be used to extend the explainable models with data about activities of daily living (ADL), physiological monitoring and other events.

The project will be part of the ACRC theme on New Technologies of Care and is aligned with other themes such as the one on data-driven insight and prediction.

The PhD position is fully funded by a studentship and the deadline for applications is the 5th of May 2021. Informal inquiries can be sent to Jacques Fleuriot (jdf@inf.ed.ac.uk).

More information and application details can be found here.

Fully Funded PhD in (AI for) Precision Medicine: Predicting rehabilitation needs and trajectories in older patients

Supervisors: Atul Anand, Jacques Fleuriot, Joanne McPeake (U of Glasgow) & Susan Shenkin

Funding: Studentship from the Precision Medicine Doctoral Training Programme.

Deadline: 7th January 2021

Our population is ageing. The majority of hospital resources are used by older and increasingly frail patients. When older people become ill with conditions like pneumonia, their ability to complete basic functions like walking and eating can be affected. Such loss of independence is a great fear for many older people. Rehabilitation tries to maximise functional recovery of older patients and restore independence. This process involves a multidisciplinary team of healthcare professionals and can be the difference between a person leaving hospital independently or requiring extensive carer support.

With the transition to electronic health records (EHRs), the quantity, timing and outcomes of hospital rehabilitation contacts are now available to drive new insights. These EHRs are central to the DataLoch, a funded linked repository of health and social care records for residents across South East Scotland. There are new opportunities to use process mining techniques, commonly applied in the business world, to understand existing trajectories of rehabilitation.

Aims

  1. To develop predictive models for estimation of rehabilitation needs using routine EHR data. This will use both standard multivariate and machine learning approaches.
  2. To use process mining of healthcare contact data to define trajectories of inpatient rehabilitation.
  3. To develop data visualisation tools for the EHR to aid patient and professional understanding of trajectories of rehabilitation in an individual.

This exciting project will develop a wide range of cutting-edge skills in healthcare data science and Explainable AI. The student will gain competences in complex predictive model development including interpretable machine learning techniques, process mining and development of interactive data visualisation tools. Many of these applications are novel and have direct translation potential to change clinical care.

The PhD position is fully funded by a studentship and the deadline for applications is Thursday 7th January 2021. Informal inquiries can be sent to Jacques Fleuriot (jdf@inf.ed.ac.uk).

More information and application details can be found here.

Research Associate in Formal Modelling and Explainable AI for Health and Care

Applications are invited for a Research Associate in Formal Modelling and Explainable AI for Health and Care in the School of Informatics, University of Edinburgh.

This post will contribute to the New Technologies of Care Programme within the Advanced Care Research Centre  ACRC) at the University of Edinburgh.

This full-time position is fixed term for 36 months.

Informal enquiries to be directed to Jacques Fleuriot (jdf@inf.ed.ac.uk) and Petros Papapanagiotou (pe.p@ed.ac.uk)

Salary grade scale: UE07 (£33,797 – £40,322pa)

Feedback is only provided to interviewed candidates.

Closing date 5pm (GMT) on 14th October 2020.

Further information

In order to allow for meaningful inference and decision making in the context of advanced care delivery for the older person, explainable computer-based models are needed that can capture the vast body of knowledge both around general and specific care pathways and incorporate data from real-time sources such as sensors. In the case of the person in later life, there is a need for contextualisation when it comes to both the care recipient and their environment.

One objective of this project is to build computer-based models that enable both the management and analysis of care, while supporting decision making via Explainable AI methods. Workflow management systems will be used to automate parts of care delivery, so that we can maintain consistent, accountable and continuous levels over time. AI-based workflow analytics and other techniques will be used to understand and predict outcomes based on sensor, video and recorded care data (coming from other streams of the New Technologies of Care Programme and the broader ACRC). Bridging the gap between the high complexity of the care landscape, the individual needs and circumstances of the older person, and the development of computer-based workflows that interface between data and processes in order to drive decision-making will be one of the important goals of this multidisciplinary project.

Further information and the application procedure are available here.