Machine Learning and Data Informatics Approaches for Personalised Outcome Prediction in Paediatric Intensive Care

This project is co-supervised by:

Tsz-Yan Milly Lo (Usher Institute of Population Health Sciences & Royal Hospital for Sick Children, Edinburgh)
Laura Moss (University of Glasgow)

This exciting multi-disciplinary PhD project will develop machine learning and informatics algorithms for data-driven linkage of clinical data in paediatric critical care settings.  We hypothesise that clinical, physiological and radiological (structural) data in paediatric patients with life-threatening brain trauma will inform about damage to the brain’s ability to auto-regulate, and that combining and mining these multimodal data will enable the detection of patients – previously unidentified – at a higher risk of poor clinical outcomes.
 
The successful applicant will do research in a multi-disciplinary and cross-College and cross-University setting at the interface of machine learning and medical informatics.  This PhD provides an excellent opportunity to be trained in quantitative and interdisciplinary skills.
 
Routine clinical practice generates a large amount of data that is under-used for research and quality improvement.  This is particularly true in paediatric intensive care units (PICU).  Yet once the patient is discharged, vital information from this physiological big data is discarded rather than being used to advance our understanding of how a patient’s physiological phenotype may affect outcome.  Lack of linkage to other data sources collected during routine clinical care (e.g., radiological images, outcome such as re-admission) prevents meaningful use of this physiology data to advance patient care and safety.  We urgently need to utilise data science to integrate the data generated from different sources during routine patient care and develop precision medicine approaches for critical care to deliver continuously improved patient care and outcome.
 
Our team has successfully defined phenotypes associated with an improved global neurological outcome through the use of data science and medical informatics.  We now seek to better understand the impact of physiological phenotypes on outcome in critically brain injured paediatric patients in order to improve their quality of life.  For this, we turn our attention to radiological imaging modalities used in the management of life-threatening Traumatic Brain Injury (TBI), and to unplanned re-admission or emergency service presentation following PICU discharge as a proxy measure of poorer quality of life.  Developing data science algorithms to link ICU clinical and physiological data with routine radiological images, unplanned hospital re-admission, or emergency service presentation offers a valuable opportunity to better understand TBI at an individual patient level. We expect this to lead the way for not only significant advances in understanding of TBI but also for personalised therapeutic strategies.

 

Further Information: 

Early application is encouraged. The advertisement may be closed once a suitable candidate is found.
 
Informal queries to Dr Javier Escudero at javier.escudero@ed.ac.uk.
 
Dr Javier Escudero
http://www.research.ed.ac.uk/portal/jescuder
http://www.eng.ed.ac.uk/about/people/dr-javier-escudero-rodriguez

Dr Tsz-Yan Milly Lo
https://www.ed.ac.uk/centre-reproductive-health/child-life-and-health/people/principal-investigators/dr-tsz-yan-milly-lo  

Dr Laura Moss
http://ideasresearch.org/lmoss.html

The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity. Please see details here: https://www.ed.ac.uk/equality-diversity

Closing Date: 

Monday, February 15, 2021

Principal Supervisor: 

Eligibility: 

Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.

Enthusiastic and self-motivated candidates are sought with a first-class undergraduate Honours degree or a master degree at 2:1 or above (or International equivalent) in electronic engineering, computer science, mathematics or cognate disciplines. An MSc qualification will be advantageous but it is not an essential requirement.
 
The candidate is expected to have very good programming and analytical skills.
 
Previous knowledge in areas related to signal processing (e.g., time series analysis), and machine learning (e.g., classification algorithms, optimisation, etc.) would be expected. Familiarity with health record data would be advantageous but it is not an essential requirement.

Funding: 

Applications are welcomed from self-funded students, or students who are applying for     scholarships from the University of Edinburgh or elsewhere.

Further information and other funding options.

Informal Enquiries: