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
Abstract
Frailty and multimorbidity becomes more prevalent as the population continues to age. We employ directed hypergraphs to represent the complex interactions among multiple health conditions and capture the inter-dependencies within multimorbidity sets. We introduce the inclusion of ‘Mortality’ nodes into directed hypergraphs. Through the analysis of ResearchOne data, we aim to identify the most prevalent combinations of frailty conditions alongside their co-occurrence with mortality, providing valuable knowledge for healthcare professionals to improve patient care and develop targeted interventions. We demonstrate that hypergraphs enable us to determine the probability of acquiring another electronic frailty index (eFI) condition, understand condition connectivity and sequentiality, and identify the most influential hyperarcs. The findings from this study suggest that hypergraphs enable us to retain progression information compared to holistic views, facilitating the implementation of more effective healthcare strategies.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG. This version of the proceedings paper has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-031-56107-8_21 |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Health Sciences (Leeds) > Centre for Health Services Research (Leeds) The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Institute of Rheumatology & Musculoskeletal Medicine (LIRMM) (Leeds) > Musculoskeletal Medicine & Imaging (Leeds) The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Health Sciences (Leeds) > Academic Unit of Elderly Care and Rehabilitation (Leeds) The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 25 Oct 2024 15:04 |
Last Modified: | 25 Oct 2024 15:04 |
Status: | Published |
Publisher: | Springer Nature |
Series Name: | Lecture Notes in Business Information Processing |
Identification Number: | 10.1007/978-3-031-56107-8_21 |
Sustainable Development Goals: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:218836 |
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