DT-ICU: Towards Explainable Digital Twins for ICU Patient Monitoring via Multi-Modal and Multi-Task Iterative Inference

TL;DR

DT-ICU is a multimodal digital twin framework for ICU patient monitoring that provides accurate, interpretable, and continuously updated risk predictions by integrating clinical time series and static data.

Contribution

This work introduces DT-ICU, a novel multi-modal, multi-task digital twin model that updates risk assessments in real-time using heterogeneous ICU data.

Findings

DT-ICU outperforms baseline models on MIMIC-IV dataset.

Early discrimination achieved shortly after ICU admission.

Model reliance on interventions, physiological responses, and context is systematically analyzed.

Abstract

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.