TRACE: Intra-visit Clinical Event Nowcasting via Effective Patient Trajectory Encoding
Journal:
arXiv
Published Date:
Mar 29, 2025
Abstract
Electronic Health Records (EHR) have become a valuable resource for a wide
range of predictive tasks in healthcare. However, existing approaches have
largely focused on inter-visit event predictions, overlooking the importance of
intra-visit nowcasting, which provides prompt clinical insights during an
ongoing patient visit. To address this gap, we introduce the task of laboratory
measurement prediction within a hospital visit. We study the laboratory data
that, however, remained underexplored in previous work. We propose TRACE, a
Transformer-based model designed for clinical event nowcasting by encoding
patient trajectories. TRACE effectively handles long sequences and captures
temporal dependencies through a novel timestamp embedding that integrates decay
properties and periodic patterns of data. Additionally, we introduce a smoothed
mask for denoising, improving the robustness of the model. Experiments on two
large-scale electronic health record datasets demonstrate that the proposed
model significantly outperforms previous methods, highlighting its potential
for improving patient care through more accurate laboratory measurement
nowcasting. The code is available at https://github.com/Amehi/TRACE.
