Hospital readmission prediction is critical for clinical decision support, aiming to identify patients at risk of returning within 30 days post-discharge. High readmission rates often indicate inadequate treatment or post-discharge care, making effective prediction models essential for optimizing resources and improving patient outcomes. We propose PT, a Transformer-based model that integrates Electronic Health Records (EHR), medical images, and clinical notes to predict 30-day all-cause hospital readmissions. PT extracts features from raw data and uses specialized Transformer blocks tailored to the data's complexity. Enhanced with Random Forest for EHR feature selection and test-time ensemble techniques, PT achieves superior accuracy, scalability, and robustness. It performs well even when temporal information is missing. Our main contributions are: (1)Simplicity: A powerful and efficient baseline model outperforming existing ones in prediction accuracy; (2)Scalability: Flexible handling of various features from different modalities, achieving high performance with just clinical notes or EHR data; (3)Robustness: Strong predictive performance even with missing or unclear temporal data.

翻译：医院再入院预测对于临床决策支持至关重要，其目标在于识别出院后30天内存在再次入院风险的患者。高再入院率通常表明治疗或出院后护理不足，因此有效的预测模型对于优化医疗资源和改善患者预后具有重要意义。我们提出PT，一种基于Transformer的模型，该模型整合电子健康记录、医学影像和临床笔记来预测30天全因医院再入院。PT从原始数据中提取特征，并使用针对数据复杂性定制的专用Transformer模块。通过结合随机森林进行EHR特征选择以及测试时集成技术增强，PT实现了优异的准确性、可扩展性和鲁棒性。即使在时间信息缺失的情况下，该模型仍能保持良好的性能。我们的主要贡献包括：(1)简洁性：构建了一个强大高效的基线模型，其预测准确性优于现有模型；(2)可扩展性：能灵活处理来自不同模态的各种特征，仅使用临床笔记或EHR数据即可实现高性能；(3)鲁棒性：即使在时间数据缺失或不明确的情况下，仍能保持强大的预测性能。