Unsupervised learning to understand patterns of comorbidity in 633,330 patients diagnosed with osteoarthritis - Pineda Moncusi, M; Strauss, VY; Robinson, DE; Prieto Alhambra, D; Khalid, S (SciTePress, 2022)
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.