- Email: firstname.lastname@example.org
- Thesis title: Development, validation and implementation of an adaptive machine learning model for perioperative mortality prediction and its adoption into combat casualty care
- Supervisor: Owen Johnson, Dr Alwyn Kotzé (Leeds Teaching Hospitals NHS Trust), Prof Geoff Hall (Leeds Teaching Hospitals NHS Trust)
Allan has been an associated PhD student on the UKRI Centre for Doctoral Training (CDT) in Artificial Intelligence for Medical Diagnosis and Care at the University of Leeds since 2021. Allan graduated from Leeds University Medical School (MBChB) in 2010 and since have served as a Medical Officer within the British Army. He previously has been part of the 16 Air Assault Brigade and was deployed to Nepal for a humanitarian disaster relief response operation following the major earthquakes in 2014. Allan was selected for military anaesthesia speciality training in 2015 within the Northern School of Anaesthesia and Intensive Care Medicine and gained his Fellowship of Royal College of Anaesthesia (FRCA) in 2019. He is part of the Academic Department of Military Anaesthesia and Critical Care (ADMACC), where he established and ran the trainee research network Tri-Service Trainee Audit & Research (Tri-STAR) group.
Allan's research concentrates on the prediction of peri-operative (i.e. surgical) outcomes. The current state of the art prediction tools produces only point probability before the surgery itself, which arguably have limited clinical utility beyond the surgical event itself. While developing a surgical prediction tool based on clinical data from Leeds Teaching Hospitals NHS Trust, he will concentrate on understanding the requirements and potential uses for a prediction tool that adapts temporally through the patient's clinical course.
- MBChB - University of Leeds
- FRCA - Royal College of Anaesthetists