Forecasting Clinical Risk from Textual Time Series: Structuring Narratives for Temporal AI in Healthcare.

ABSTRACT: Clinical case reports encode temporal patient trajectories that are often underexploited by traditional machine learning methods relying on structured data. In this work, we introduce the forecasting problem from textual time series, where timestamped clinical findings-extracted via an LLM-assisted annotation pipeline-serve as the primary input for prediction. We systematically evaluate a diverse suite of models, including fine-tuned decoder-based large language models and encoder-based transformers, on tasks of event occurrence prediction, temporal ordering, and survival analysis. Our experiments reveal that encoder-based models consistently achieve higher F1 scores and superior temporal concordance for short- and long-horizon event forecasting, while fine-tuned masking approaches enhance

SUBMITTER: Noroozizadeh S 

PROVIDER: S-EPMC12747250 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature