通过逐日建模多模态数据，提升30天再入院预测准确率。
Mr.Dec: Daily-Scale Longitudinal Multimodal Modeling for 30-Day Readmission Prediction
- 以每日为单位建模电子病历与胸片数据的时序变化
- 在MIMIC-IV和MIMIC-CXR上达到当前最优性能
- 识别出关键风险日，支持临床实时决策
预测30天内再入院对评估患者稳定性和优化医疗资源至关重要。随着住院过程推进，临床风险动态演变，捕捉这种长期轨迹十分关键。然而，现有方法常将复杂的纵向历史压缩为固定表示，丢失反映生理状态演变的细粒度日级信号。为此，我们提出Mr.Dec（多模态再入院风险解码器），将每次住院视为自然的时间序列，整合每日电子健康记录（EHR）更新与间断性胸部X光（CXR）结果，实现时间对齐的多模态融合。通过使用疾病特异性监督对比学习作为辅助正则化，增强潜在空间中的诊断感知结构。在MIMIC-IV和MIMIC-CXR数据集上的实验表明，Mr.Dec通过保留临床序列完整性，实现了当前最优表现。此外，模型可识别住院期间的“关键日”，为实时风险分层提供可解释、临床可落地的分析。代码已公开于：https://github.com/yejix-ai/MR.DEC
原文摘要 · Abstract (English)
Predicting 30-day hospital readmission is essential for assessing patient stability and optimizing healthcare resources. As clinical risk evolves with the accumulation of evidence during hospitalization, capturing these dynamic trajectories is essential. However, many existing approaches compress the complex longitudinal history into fixed representations, often losing the granular, day-level clinical signals that reflect a patient's evolving physiological state. To address this, we propose Mr.Dec (Multimodal Readmission-risk prediction Decoder), which models each admission as a natural chronological sequence of daily multimodal events. By leveraging a Transformer Decoder, Mr.Dec integrates daily Electronic Health Record(EHR) updates and intermittent Chest X-ray(CXR) findings in a time-aligned stream, reflecting the actual clinical workflow. To ensure robustness, we utilize Disease-Specific Supervised Contrastive Learning as an auxiliary regularization to induce a diagnosis-aware structure in the latent space. Evaluations on the MIMIC-IV and MIMIC-CXR datasets show that Mr.Dec achieves state-of-the-art performance by preserving the integrity of the clinical sequence. Furthermore, our model identifies "Critical Days" within an admission, providing actionable and clinically grounded interpretations for real-time risk stratification. Code is available at: https://github.com/yejix-ai/MR.DEC
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