Vincent S. Tseng

Branching motifs in EHRs offer structured trajectory context that primarily enhances long-range temporal forecasting, rather than uniformly improving endpoint discrimination or survival prediction.

Novel disease trajectories discovered from EHRs can be effectively prioritized using a two-level semantic sparsity and reliability framework, leading to significant improvements in clinical forecasting accuracy.

FETERS achieves state-of-the-art early time-series classification with just five labeled examples, outperforming existing methods on 44 datasets.

TRACE-CASH outperforms six leading optimization methods, achieving superior performance in time-series forecasting tasks.