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.
- Corrigendum “Anticoagulation for atrial fibrillation in people with serious mental illness in the general hospital setting"
- Foresight -- Deep Generative Modelling of Patient Timelines using Electronic Health Records
- Diagnostic signature for heart failure with preserved ejection fraction (HFpEF): a machine learning approach using multi-modality electronic health record data
- Hospital-wide Natural Language Processing summarising the health data of 1 million patients
- Anticoagulation for atrial fibrillation in people with serious mental illness in the general hospital setting