We present a new Hebrew medical language model designed to extract structured clinical timelines from electronic health records, enabling the construction of patient journeys. Our model is based on DictaBERT 2.0 and continually pre-trained on over five million de-identified hospital records. To evaluate its effectiveness, we introduce two new datasets -- one from internal medicine and emergency departments, and another from oncology -- annotated for event temporal relations. Our results show that our model achieves strong performance on both datasets. We also find that vocabulary adaptation improves token efficiency and that de-identification does not compromise downstream performance, supporting privacy-conscious model development. The model is made available for research use under ethical restrictions.

翻译：我们提出了一种新的希伯来语医学语言模型，旨在从电子健康记录中提取结构化临床时间线，从而构建患者旅程。该模型基于DictaBERT 2.0架构，并持续预训练了超过五百万条去识别化的医院记录。为评估其有效性，我们引入了两个新数据集——一个来自内科和急诊科，另一个来自肿瘤科——均标注了事件时序关系。实验结果表明，我们的模型在两个数据集上均表现出色。我们还发现，词汇适应提高了分词效率，且去识别化处理不会损害下游任务性能，这支持了注重隐私的模型开发。该模型在伦理限制下可供研究使用。