In the framework of precision medicine, we investigate the similarity of diabetic kidney disease (DKD) patients through longitudinal data clusters. Starting with insights from category theory, we build patients’ clusters according to the shapes of their trajectories, adopting the Fréchet distance. We group patients according to their behavior of the estimated glomerular filtration rate (eGFR), obtaining informative mean curves. Behavior pattern recognition can shed light on individualized treatments.

Clustering longitudinal data with category theory for diabetic kidney disease

Maria Mannone
;
Veronica Distefano;Claudio Silvestri;Irene Poli
2021-01-01

Abstract

In the framework of precision medicine, we investigate the similarity of diabetic kidney disease (DKD) patients through longitudinal data clusters. Starting with insights from category theory, we build patients’ clusters according to the shapes of their trajectories, adopting the Fréchet distance. We group patients according to their behavior of the estimated glomerular filtration rate (eGFR), obtaining informative mean curves. Behavior pattern recognition can shed light on individualized treatments.
Scientific Meeting of the Classification and Data Analysis Group
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/3743270
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