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Biography

Mathew Lyons is an Anaesthetics Trainee working in South-East Scotland and a SCREDS Clinical Lecturer in Anaesthesia. He has a keen research interest in medical informatics, machine learning, and the interface between engineering and medicine. His lectureship project is using data techniques to explore mental health in pregnancy.

Mathew holds an MBChB with Honours from the University of Edinburgh. Prior to his medical training, he completed a PhD in Bioengineering at Trinity College Dublin, focusing on the mechanics of wound closure for laparoscopic surgery. His academic background also includes a Postgraduate Diploma in Statistics and a degree in Mechanical and Manufacturing Engineering, both from Trinity College Dublin, where he was elected to the Scholarship of the University of Dublin.

His work includes extensive studies on inflammatory bowel disease (IBD), where he has contributed to epidemiological research and investigations into biologic therapies and developing prospective registries for IBD patients with data linkage studies to better understand disease prevalence and treatment outcomes. His research has also explores the use of machine learning methods for predictive modelling in perioperative medicine.

As an educator, Mathew is involved in teaching at various levels. He contributes to the education of undergraduate medical students and postgraduate trainees, and serves as a tutor for several courses. He is a co-founder of MeFB, a feedback tool for medical educators (medicalfeedback.org).

Mathew is currently engaged in several ongoing research projects including studies on machine learning methods for predictive modelling in perioperative medicine and a project quantifying exhaled aerosolised respiratory viruses in acutely hospitalised patients. His work often bridges the gap between clinical practice and engineering.

Qualifications

BA BAI MBChB PGDip PhD Sch.

Current Research Interests

  1. Medical Informatics and Machine Learning Applying advanced computational techniques to healthcare challenges. This includes using machine learning algorithms for predictive modelling in perioperative medicine, especially in vascular surgery outcomes.

 

  1. Epidemiology Development of patient registries, and data-based investigations into therapies by using multi-source healthcare data linkage to better understand disease prevalence, treatment outcomes, and long-term patient trajectories.

 

  1. Perioperative Medicine Improving patient outcomes through better understanding and prediction of surgical risks and using machine learning methods to predict outcomes.

 

  1. Translational Engineering in Medicine Bridging the engineering and clinical practice interface.

External positions

Specialty Registrar in Anaesthetics, NHS Lothian

Aug 2024 → …

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