Unsupervised learning to understand patterns of comorbidity in 633,330 patients diagnosed with osteoarthritis
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....
| Үндсэн зохиолчид: | , , , , |
|---|---|
| Формат: | Conference item |
| Хэл сонгох: | English |
| Хэвлэсэн: |
SciTePress
2022
|
| Тойм: | 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. |
|---|