
Dr Daniel Bean
HDRUK/UKRI Innovation Fellow
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BIOGRAPHY
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.
RESEARCH INTERESTS
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.
LATEST PUBLICATIONS
- A Knowledge Distillation Ensemble Framework for Predicting Short- and Long-Term Hospitalization Outcomes from Electronic Health Records Data
- A Knowledge Distillation Ensemble Framework for Predicting Short and Long-term Hospitalisation Outcomes from Electronic Health Records Data
- A novel algorithmic approach to generate consensus treatment guidelines in adult acute myeloid leukaemia
- Mapping Multimorbidity in Individuals with Schizophrenia and Bipolar Disorders: Evidence from the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLAM BRC) Case Register
- Investigating the Association between Physical Health Comorbidities and Disability in Individuals with Severe Mental Illness