Unsupervised Learning to Understand Patterns of Comorbidity in 633,330 Patients Diagnosed with Osteoarthritis

Marta Pineda-Moncusi, Victoria Strauss, Danielle Robinson, Daniel Prieto-Alhambra, Sara Khalid

2022

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 Harvard Style

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 - Volume 3: BIOINFORMATICS, ISBN 978-989-758-552-4, pages 121-129. DOI: 10.5220/0010833500003123


in Bibtex Style

@conference{bioinformatics22,
author={Marta Pineda-Moncusi and Victoria Strauss and Danielle 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 - Volume 3: BIOINFORMATICS,},
year={2022},
pages={121-129},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010833500003123},
isbn={978-989-758-552-4},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS,
TI - Unsupervised Learning to Understand Patterns of Comorbidity in 633,330 Patients Diagnosed with Osteoarthritis
SN - 978-989-758-552-4
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