We introduce DT-ICU, a multimodal digital twin framework for continuous risk estimation in intensive care. DT-ICU integrates variable-length clinical time series with static patient information in a unified multitask architecture, enabling predictions to be updated as new observations accumulate over the ICU stay. We evaluate DT-ICU on the large, publicly available MIMIC-IV dataset, where it consistently outperforms established baseline models under different evaluation settings. Our test-length analysis shows that meaningful discrimination is achieved shortly after admission, while longer observation windows further improve the ranking of high-risk patients in highly imbalanced cohorts. To examine how the model leverages heterogeneous data sources, we perform systematic modality ablations, revealing that the model learnt a reasonable structured reliance on interventions, physiological response observations, and contextual information. These analyses provide interpretable insights into how multimodal signals are combined and how trade-offs between sensitivity and precision emerge. Together, these results demonstrate that DT-ICU delivers accurate, temporally robust, and interpretable predictions, supporting its potential as a practical digital twin framework for continuous patient monitoring in critical care. The source code and trained model weights for DT-ICU are publicly available at https://github.com/GUO-W/DT-ICU-release.

翻译：本文提出DT-ICU，一种用于重症监护连续风险估计的多模态数字孪生框架。DT-ICU将可变长度的临床时间序列与静态患者信息集成于统一的多任务架构中，使得预测能够随着ICU住院期间新观测数据的积累而持续更新。我们在大型公开数据集MIMIC-IV上评估DT-ICU，其在不同评估设定下均稳定优于现有基线模型。我们的观测时长分析表明，模型在患者入院后短期内即可实现有效的风险区分，而更长的观测窗口能进一步提升其在高度不平衡队列中对高风险患者的排序能力。为探究模型如何利用异构数据源，我们进行了系统的模态消融实验，结果显示模型习得了对干预措施、生理反应观测与上下文信息的合理结构化依赖。这些分析为多模态信号如何融合、以及敏感性与精确度之间的权衡如何形成提供了可解释的洞察。综上，本研究证明DT-ICU能够提供准确、时序鲁棒且可解释的预测，展现了其作为危重症护理中连续患者监护实用数字孪生框架的潜力。DT-ICU的源代码与训练模型权重已公开于https://github.com/GUO-W/DT-ICU-release。