I am a UKRI Innovation Fellow funded by Health Data Research UK. I work closely with clinical collaborators across King’s Health Partners applying machine learning to patient records at scale. My research develops machine learning methods based on knowledge graphs that combine large public datasets with health records to predict and explain patient outcomes. The focus is on delivering real world clinical value through multiple clinical collaborations including atrial fibrillation management, patient flow, adverse drug reactions, cancer subtyping and kidney failure. I did my PhD at Cambridge University where I worked on applying systems biology methods to neurodegenerative disease, then I joined the Dobson group in 2016 as a postdoctoral research fellow in the NIHR Maudsley BRC.
Using knowledge graphs to improve machine learning performance; Putting machine learning into clinical practice with explainability and real-time support; Natural language processing methods for clinical text; Modelling patient trajectories.
- The Effects of ARBs, ACEis, and Statins on Clinical Outcomes of COVID-19 Infection Among Nursing Home Residents
- Angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers are not associated with severe COVID-19 infection in a multi-site UK acute hospital trust
- ACE‐inhibitors and Angiotensin‐2 Receptor Blockers are not associated with severe SARS‐COVID19 infection in a multi‐site UK acute Hospital Trust
- A knowledge-based machine learning approach to gene prioritisation in Amyotrophic Lateral Sclerosis
- MedCATTrainer: A biomedical free text annotation interface with active learning and research use case specific customisation