Mr.Dec：用于30天再入院预测的日尺度纵向多模态建模
Mr.Dec: Daily-Scale Longitudinal Multimodal Modeling for 30-Day Readmission Prediction
- Yeji X
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出Mr.Dec模型，通过Transformer解码器整合每日EHR与CXR数据，结合疾病特异性监督对比学习，在MIMIC-IV等数据集上实现30天再入院预测的SOTA性能，还可识别住院关键天数提供临床解释。
AI中文摘要：
预测患者30天内的医院再入院情况，对于评估患者病情稳定性和优化医疗资源配置至关重要。由于临床风险会随住院期间证据的积累而变化，捕捉这些动态轨迹是必要的。然而，许多现有方法会将复杂的纵向病史压缩为固定表示，往往会丢失反映患者生理状态演变的细粒度日级别临床信号。为解决这一问题，我们提出了Mr.Dec（多模态再入院风险预测解码器），该模型将每次住院过程建模为每日多模态事件的自然时间序列。通过利用Transformer解码器，Mr.Dec在时间对齐的数据流中整合每日电子健康记录（EHR）更新和间歇性胸部X线（CXR）检查结果，贴合实际临床工作流程。为确保模型鲁棒性，我们采用疾病特异性监督对比学习作为辅助正则化，在潜在空间中引入诊断感知结构。在MIMIC-IV和MIMIC-CXR数据集上的评估显示，Mr.Dec通过保留临床序列的完整性实现了最先进的性能。此外，我们的模型还能识别住院期间的“关键天数”，为实时风险分层提供可操作且基于临床依据的解释。代码可在指定链接获取。
英文摘要：
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
