arXiv cs.LG论文
Mr.Dec：用于30天再入院预测的日尺度纵向多模态建模
本文提出Mr.Dec，一种基于Transformer解码器的多模态模型，用于预测患者30天内再入院风险。该模型将住院期间每日的电子健康记录（EHR）更新和间歇性胸部X光（CXR）结果按时间顺序对齐，保留日级临床信号，并通过疾病特异性监督对比学习增强表征。在MIMIC-IV和MIMIC-CXR数据集上达到最先进性能，并能识别住院期间的“关键日”，为实时风险分层提供可解释的依据。
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Abstract: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 this http URL (Multimodal Readmission-risk prediction Decoder), which models each admission as a natural chronological sequence of daily multimodal events. By leveraging a Transformer Decoder, this http URL 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 this http URL 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: this https URL