融合临床文本与不规则时间序列，提升肾移植术后风险预测
Temporal Fusion Nexus: A task-agnostic multi-modal embedding model for clinical narratives and irregular time series in post-kidney transplant care
- 用多模态嵌入统一处理病历文本与不规则时间数据
- 在移植物丢失、排斥和死亡预测上均优于现有模型，最高AUC达0.96
- 结果可解释，适合作为临床决策支持工具
我们提出Temporal Fusion Nexus（TFN），一种多模态、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病历。在3382名肾移植患者的回顾性队列中，评估了移植物丢失、排斥和死亡三个关键结局。相比现有最优模型，TFN在移植物丢失预测中表现更优（AUC 0.96 vs. 0.94），在排斥预测中显著领先（AUC 0.84 vs. 0.74），死亡预测达到AUC 0.86。相较于仅使用时间序列的基线模型，性能提升约10%；结合静态患者数据后提升约5%。临床文本的引入进一步改善所有任务的表现。解耦度量验证了嵌入空间中鲁棒且可解释的潜在因子，SHAP归因分析显示模型决策与临床逻辑一致。该模型可推广至其他存在异构数据、不规则纵向数据与丰富文书记录的临床场景。
原文摘要 · Abstract (English)
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.
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