Clinical case reports encode rich, 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 ranking performance. In contrast, instruction-tuned decoder models demonstrate a relative advantage in survival analysis, especially in early prognosis settings. Our sensitivity analyses further demonstrate the importance of time ordering, which requires clinical time series construction, as compared to text ordering, the format of the text inputs that LLMs are classically trained on. This highlights the additional benefit that can be ascertained from time-ordered corpora, with implications for temporal tasks in the era of widespread LLM use.

本研究解决了传统机器学习方法对未经充分利用的临床文本时间序列的预测问题。通过引入基于时间戳的临床发现作为主要输入进行预测，实验表明编码器模型在短期和长期事件预测中表现优异，且时间排序的构建显著提升了模型性能。该研究的发现强调了时间序列构建的重要性，为大规模语言模型在时间任务中的应用提供了新的视角。

临床文本时间序列的预测：编码器和解码器语言模型家族的适应性