Postoperative complications remain a critical concern in clinical practice, adversely affecting patient outcomes and contributing to rising healthcare costs. We present MIRACLE, a deep learning architecture for prediction of risk of postoperative complications in lung cancer surgery by integrating preoperative clinical and radiological data. MIRACLE employs a hyperspherical embedding space fusion of heterogeneous inputs, enabling the extraction of robust, discriminative features from both structured clinical records and high-dimensional radiological images. To enhance transparency of prediction and clinical utility, we incorporate an interventional deep learning module in MIRACLE, that not only refines predictions but also provides interpretable and actionable insights, allowing domain experts to interactively adjust recommendations based on clinical expertise. We validate our approach on POC-L, a real-world dataset comprising 3,094 lung cancer patients who underwent surgery at Roswell Park Comprehensive Cancer Center. Our results demonstrate that MIRACLE outperforms various traditional machine learning models and contemporary large language models (LLM) variants alone, for personalized and explainable postoperative risk management.

翻译：术后并发症仍是临床实践中的关键问题，对患者预后产生不利影响并导致医疗成本上升。本文提出MIRACLE深度学习架构，通过整合术前临床与影像学数据实现肺癌术后并发症风险预测。MIRACLE采用超球面嵌入空间融合异构输入，能够从结构化临床记录和高维影像数据中提取鲁棒的判别性特征。为提升预测透明度和临床实用性，我们在MIRACLE中嵌入了干预式深度学习模块，该模块不仅能优化预测结果，还可提供可解释且可操作的临床洞见，使领域专家能基于临床知识交互式调整推荐方案。我们在POC-L真实世界数据集上验证了该方法，该数据集包含罗斯威尔公园综合癌症中心接受手术的3,094例肺癌患者。实验结果表明，在个性化与可解释的术后风险管理任务中，MIRACLE优于多种传统机器学习模型及当前大型语言模型（LLM）的独立变体。