Unsupervised learning to understand patterns of comorbidity in 633,330 patients diagnosed with osteoarthritis

Conference item

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.

Authors: Pineda Moncusi, M; Strauss, VY; Robinson, DE; Prieto Alhambra, D; Khalid, S

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: SciTePress
• Host title: Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOINFORMATICS
• Volume: 3
• Pages: 121-129
• Publication date: 2022-02-24
• Acceptance date: 2021-11-15
• Event title: BIOSTEC 2022
• Event location: Virtual Event
• Event website: https://biostec.scitevents.org/
• Event start date: 2022-02-09
• Event end date: 2022-02-11
• DOI: 10.5220/0010833500003123
• ISSN: 2184-4305
• ISBN: 978-989-758-552-4
• Language: English
• Keywords: data mining; electronic healthcare records; FFR; comorbidity pattern; osteoarthritis; cluster analysis