journal article
Open Access
Jul 05, 2023
Exploring Heterogeneity with Category and Cluster Analyses for Mixed Data
Abstract
Precision medicine aims to overcome the traditional one-model-fits-the-whole-population approach that is unable to detect heterogeneous disease patterns and make accurate personalized predictions. Heterogeneity is particularly relevant for patients with complications of type 2 diabetes, including diabetic kidney disease (DKD). We focus on a DKD longitudinal dataset, aiming to find specific subgroups of patients with characteristics that have a close response to the therapeutic treatment. We develop an approach based on some particular concepts of category theory and cluster analysis to explore individualized modelings and achieving insights onto disease evolution. This paper exploits the visualization tools provided by category theory, and bridges category-based abstract works and real datasets. We build subgroups deriving clusters of patients at different time points, considering a set of variables characterizing the state of patients. We analyze how specific variables affect the disease progress, and which drug combinations are more effective for each cluster of patients. The retrieved information can foster individualized strategies for DKD treatment.
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Metrics
6
Citations
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References
Details
- Published
- Jul 05, 2023
- Vol/Issue
- 6(3)
- Pages
- 747-762
- License
- View
Authors
Funding
European Union’s Horizon 2020 research and innovation program
Award: 848011
Cite This Article
Veronica Distefano, Maria Mannone, Irene Poli (2023). Exploring Heterogeneity with Category and Cluster Analyses for Mixed Data. Stats, 6(3), 747-762. https://doi.org/10.3390/stats6030048
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