用多模态融合与可干预设计，提升肺癌术后并发症预测的准确性与可解释性。
LLM Augmented Intervenable Multimodal Adaptor for Post-operative Complication Prediction in Lung Cancer Surgery
- 将临床数据与影像数据在球面空间融合，提取鲁棒特征
- 在3094例患者上表现优于传统模型和纯LLM方法
- 支持医生交互调整，适合临床决策场景
术后并发症仍是临床实践中的关键问题，影响患者预后并推高医疗成本。我们提出MIRACLE，一种基于深度学习的肺癌手术术后并发症风险预测架构，整合术前临床与影像数据。MIRACLE采用异构输入的超球面嵌入空间融合，从结构化临床记录和高维影像中提取稳健、具有区分性的特征。为增强预测透明度与临床实用性，我们在MIRACLE中引入可干预深度学习模块，不仅能优化预测结果，还能提供可解释、可操作的洞察，使领域专家可根据临床经验交互式调整建议。我们在包含3,094例患者的真实世界数据集POC-L上验证该方法，结果表明MIRACLE在个性化与可解释的风险管理方面优于多种传统机器学习模型及独立使用的主流大语言模型（LLM）变体。
原文摘要 · Abstract (English)
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.
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