David Clifton

Addressing label noise for electronic health records: insights from computer vision for tabular data.

An improved deep regression model with state space reconstruction for continuous blood pressure estimation.

Atrial fibrillation after cardiac surgery: identifying candidate predictors through a Delphi process.

CliqueFluxNet: Unveiling EHR Insights with Stochastic Edge Fluxing and Maximal Clique Utilisation Using Graph Neural Networks.

Continuous patient state attention model for addressing irregularity in electronic health records.

Decoding 2.3 million ECGs: interpretable deep learning for advancing cardiovascular diagnosis and mortality risk stratification.

DuKA: A Dual-Keyless-Attention Model for Multi-modality EHR Data Fusion and Organ Failure Prediction.

Efficiency at scale: Investigating the performance of diminutive language models in clinical tasks.

Exploring the effectiveness of instruction tuning in biomedical language processing.

Generalizability assessment of AI models across hospitals in a low-middle and high income country.

Knowledge abstraction and filtering based federated learning over heterogeneous data views in healthcare.

Masks and Manuscripts: Advancing Medical Pre-training with End-to-End Masking and Narrative Structuring.