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.
Group- based multi-trajectory modeling.Statistical Methods in Medical Research, 27(7):2015– 2023, 2018
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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.