We introduce Temporal Fusion Nexus (TFN), a multi-modal and task-agnostic embedding model to integrate irregular time series and unstructured clinical narratives. We analysed TFN in post-kidney transplant (KTx) care, with a retrospective cohort of 3382 patients, on three key outcomes: graft loss, graft rejection, and mortality. Compared to state-of-the-art model in post KTx care, TFN achieved higher performance for graft loss (AUC 0.96 vs. 0.94) and graft rejection (AUC 0.84 vs. 0.74). In mortality prediction, TFN yielded an AUC of 0.86. TFN outperformed unimodal baselines (approx 10% AUC improvement over time series only baseline, approx 5% AUC improvement over time series with static patient data). Integrating clinical text improved performance across all tasks. Disentanglement metrics confirmed robust and interpretable latent factors in the embedding space, and SHAP-based attributions confirmed alignment with clinical reasoning. TFN has potential application in clinical tasks beyond KTx, where heterogeneous data sources, irregular longitudinal data, and rich narrative documentation are available.

翻译：我们提出了时序融合枢纽（TFN），一种多模态且任务无关的嵌入模型，用于整合不规则时间序列与非结构化临床叙事。我们在肾移植（KTx）术后护理中评估了TFN，使用了一个包含3382名患者的回顾性队列，针对三个关键结局：移植物丢失、移植物排斥和死亡。与肾移植术后护理领域的最先进模型相比，TFN在移植物丢失（AUC 0.96 对比 0.94）和移植物排斥（AUC 0.84 对比 0.74）预测上取得了更高的性能。在死亡率预测中，TFN的AUC为0.86。TFN的表现优于单模态基线（相比仅使用时间序列的基线AUC提升约10%，相比时间序列结合静态患者数据的基线AUC提升约5%）。整合临床文本提升了所有任务的性能。解纠缠度量证实了嵌入空间中稳健且可解释的潜在因子，基于SHAP的归因分析也证实了其与临床推理的一致性。TFN在肾移植以外的临床任务中具有潜在应用价值，适用于存在异构数据源、不规则纵向数据以及丰富叙事性文档的场景。