Phenotyping and Disease Subtyping

We use unsupervised learning and time-series clustering to discover hidden disease subtypes and patient cohorts. This enables precise diagnostics, risk stratification, and targeted treatment in complex and heterogeneous conditions.

Publications

Vukosavljević, Katarina, et al. “CAMELOT++: Enhancing Patient Phenotype Discovery through Time-Series Clustering and Survival Analysis.” Women in Machine Learning (WiML) Workshop at NeurIPS, 2024. poster
Aguiar, Henrique, et al. "Learning of cluster-based feature importance for electronic health record time-series." International conference on machine learning. PMLR, 2022. paper