Daniel Bean

Daniel Bean is a researcher specializing in Natural Language Processing (NLP) and Electronic Health Records (EHRs), with a focus on Medical Informatics and Clinical Data Modeling. His work intersects Artificial Intelligence and Healthcare Innovation, particularly in extracting value from large-scale patient data.

• Research Themes
  • Transformer-based generative models for patient timeline prediction
  • Scalable NLP systems for hospital-wide EHR summarization
  • Knowledge distillation frameworks for hospitalization outcome prediction
  • Statistical methodologies in trial emulation for medical research

Project Leadership: Bean leads research initiatives funded by

• BHF (British Heart Foundation, 2023-2025) for AI in heart failure detection
• MRC (Medical Research Council, 2 scholarly years) on knowledge graph learning
• EPSRC (short-term 2021 grant) for patient outcome modeling

Academic Impact: With over 1000 citations, his work appears in top journals like The Lancet Digital Health, PLOS Digital Health, and IEEE Journal of Biomedical and Health Informatics. His research has been blogged, retweeted by 60+ social media users, and discussed in 32 news outlets.