Items where authors include "Hancox, Z."
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Syversen, A., Umney, O., Howell, L. et al. (8 more authors) (2025) “How can we involve Patients?” - Students’ perspectives on embedding PPIE into a doctoral training centre for AI in medical diagnosis and care. Research Involvement and Engagement, 11. 77. ISSN 2056-7529
Hancox, Z. orcid.org/0000-0003-0473-5971, Kingsbury, S.R. orcid.org/0000-0002-9917-1269, Conaghan, P.G. orcid.org/0000-0002-3478-5665 et al. (2 more authors) (2025) Primary care prediction of hip and knee replacement 1-5 years in advance using temporal graph-based convolutional neural networks (TG-CNNs). Rheumatology. keaf185. ISSN 1462-0324
Hancox, Z. orcid.org/0000-0003-0473-5971, Pang, A. orcid.org/0009-0008-2930-6077, Conaghan, P.G. orcid.org/0000-0002-3478-5665 et al. (3 more authors) (2024) A systematic review of networks for prognostic prediction of health outcomes and diagnostic prediction of health conditions within Electronic Health Records. Artificial Intelligence in Medicine, 158. 102999. ISSN 0933-3657
Proceedings Paper
Hancox, Z., Kingsbury, S.R., Clegg, A. orcid.org/0000-0001-5972-1097 et al. (2 more authors) (2025) Developing the Temporal Graph Convolutional Neural Network Model to Predict Hip Replacement using Electronic Health Records. In: 2024 International Conference on Machine Learning and Applications (ICMLA). 23rd International Conference on Machine Learning and Applications, 18-20 Dec 2024, Miami, Florida, USA. IEEE , pp. 256-263. ISBN 979-8-3503-7489-6
Hancox, Z. orcid.org/0000-0003-0473-5971, Relton, S.D. orcid.org/0000-0003-0634-4587, Clegg, A. orcid.org/0000-0001-5972-1097 et al. (2 more authors) (2024) Hypergraphs for Frailty Analysis Research Paper. In: Process Mining Workshops. 5th International Conference on Process Mining (ICPM 2023), 23-27 Oct 2023, Rome, Italy. Lecture Notes in Business Information Processing, 503 . Springer Nature , pp. 271-282. ISBN 9783031561061
Hancox, Z. orcid.org/0000-0003-0473-5971 and Relton, S.D. orcid.org/0000-0003-0634-4587 (2022) Temporal Graph-Based CNNs (TG-CNNs) for Online Course Dropout Prediction. In: ISMIS 2022: Foundations of Intelligent Systems. Foundations of Intelligent Systems, 26th International Symposium, ISMIS 2022, 2022-10-3 - 2022-10-5, Cosenza, Italy. Lecture Notes in Computer Science, 13515 . Springer , pp. 357-367. ISBN 978-3-031-16563-4