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Scientific Reports|July 13, 2021
Interpretable deep learning for the remote characterisation of ambulation in multiple sclerosis using smartphonesAndrew P Creagh, Florian Lipsmeier, Michael Lindemann, et al.IEEE Open Journal of Engineering in Medicine and Biology|December 29, 2022
Longitudinal Trend Monitoring of Multiple Sclerosis Ambulation Using SmartphonesAndrew P Creagh, Frank Dondelinger, Florian Lipsmeier, et al.Journal of the American Medical Directors Association|September 14, 2017
Baseline Association of Motoric Cognitive Risk Syndrome With Sustained Attention, Memory, and Global CognitionFiachra J Maguire, Isabelle Killane, Andrew P Creagh, et al.NPJ Digital Medicine|April 12, 2024
Self-supervised learning for human activity recognition using 700,000 person-days of wearable dataHang Yuan, Shing Chan, Andrew P Creagh, et al.Sensors (Basel, Switzerland)|September 28, 2023
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 CountriesPing Lu, Andrew P Creagh, Huiqi Y Lu, et al.The Lancet. Rheumatology|August 1, 2024
Digital health technologies to strengthen patient-centred outcome assessment in clinical trials in inflammatory arthritisDylan McGagh, Kaiyang Song, Hang Yuan, et al.IEEE Journal of Biomedical and Health Informatics|August 6, 2020
Smartphone- and Smartwatch-Based Remote Characterisation of Ambulation in Multiple Sclerosis During the Two-Minute Walk TestAndrew P Creagh, Cedric Simillion, Alan K Bourke, et al.NPJ Digital Medicine|February 12, 2024
Digital health technologies and machine learning augment patient reported outcomes to remotely characterise rheumatoid arthritisAndrew P Creagh, Valentin Hamy, Hang Yuan, et al.Pageof 1