LOPEL learns patient-level embeddings of longitudinal medication trajectories using an ATC-informed tree kernel and a Wasserstein-based trajectory kernel, and the embeddings recover simulated clusters and identify four PWH subgroups.
Anatomical therapeutic chemical classification system (ATC)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
method 1
citation-polarity summary
fields
stat.ME 1years
2026 1verdicts
CONDITIONAL 1roles
method 1polarities
use method 1representative citing papers
citing papers explorer
-
Self-Supervised Representation Learning for Longitudinal Polypharmacy Patterns
LOPEL learns patient-level embeddings of longitudinal medication trajectories using an ATC-informed tree kernel and a Wasserstein-based trajectory kernel, and the embeddings recover simulated clusters and identify four PWH subgroups.