Machine learning and data informatics approaches for Personalised Outcome Prediction in Paediatric Intensive Care

This 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.

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.