Interpretable deep learning for the remote characterisation of ambulation in multiple sclerosis using smartphones by Creagh, AP, Lipsmeier, F, Lindemann, M, Vos, MD

2D-WinSpatt-Net: a dual spatial self-attention Vision Transformer boosts classification of tetanus severity for patients wearing ECG sensors in low- and middle-income countries by Lu, P, Creagh, AP, Lu, HY, Hai, HB, Thwaites, L, Clifton, DA

Self-supervised learning for human activity recognition using 700,000 person-days of wearable data by Yuan, H, Chan, S, Creagh, AP, Tong, C, Acquah, A, Clifton, DA, Doherty, A

Smartphone- and smartwatch-based remote characterisation of ambulation in multiple sclerosis during the two-minute walk test by Creagh, AP, Simillion, C, Bourke, AK, Scotland, A, Lipsmeier, F, Bernasconi, C, van Beek, J, Baker, M, Gossens, C, Lindemann, M, De Vos, M

Digital health technologies and machine learning augment patient reported outcomes to remotely characterise rheumatoid arthritis by Creagh, AP, Hamy, V, Yuan, H, Mertes, G, Tomlinson, R, Chen, W-H, Williams, R, Llop, C, Yee, C, Duh, MS, Doherty, A, Garcia-Gancedo, L, Clifton, DA

Digital health technologies and machine learning augment patient reported outcomes to remotely characterise rheumatoid arthritis by Creagh, AP, Hamy, V, Yuan, H, Mertes, G, Tomlinson, R, Chen, W-H, Williams, R, Llop, C, Yee, C, Duh, MS, Doherty, A, Garcia-Gancedo, L, Clifton, DA