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Authors: Marta Pineda-Moncusi ; Victoria Y. Strauss ; Danielle E. Robinson ; Daniel Prieto-Alhambra and Sara Khalid

Affiliation: Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford, U.K.

Keyword(s): Cluster Analysis, Electronic Healthcare Records, Osteoarthritis, Comorbidity Pattern, Data Mining.

Abstract: With the advent of big data in healthcare, machine learning has rapidly gained popularity due to its potential to analyse large volumes of complex data from a variety of sources. Unsupervised learning can be used to mine data and discover patterns such as sub-groups within large patient populations. However challenges with implementation in large-scale datasets and interpretability of solutions in a real-world context remain. This work presents an application of unsupervised clustering techniques for discovering patterns of comorbidities in a large dataset of osteoarthritis patients with a view to discover interpretable and clinically-meaningful patterns.

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Paper citation in several formats:
Pineda-Moncusi, M.; Strauss, V.; Robinson, D.; Prieto-Alhambra, D. and Khalid, S. (2022). Unsupervised Learning to Understand Patterns of Comorbidity in 633,330 Patients Diagnosed with Osteoarthritis. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - BIOINFORMATICS; ISBN 978-989-758-552-4; ISSN 2184-4305, SciTePress, pages 121-129. DOI: 10.5220/0010833500003123

@conference{bioinformatics22,
author={Marta Pineda{-}Moncusi. and Victoria Y. Strauss. and Danielle E. Robinson. and Daniel Prieto{-}Alhambra. and Sara Khalid.},
title={Unsupervised Learning to Understand Patterns of Comorbidity in 633,330 Patients Diagnosed with Osteoarthritis},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - BIOINFORMATICS},
year={2022},
pages={121-129},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010833500003123},
isbn={978-989-758-552-4},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - BIOINFORMATICS
TI - Unsupervised Learning to Understand Patterns of Comorbidity in 633,330 Patients Diagnosed with Osteoarthritis
SN - 978-989-758-552-4
IS - 2184-4305
AU - Pineda-Moncusi, M.
AU - Strauss, V.
AU - Robinson, D.
AU - Prieto-Alhambra, D.
AU - Khalid, S.
PY - 2022
SP - 121
EP - 129
DO - 10.5220/0010833500003123
PB - SciTePress

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