用贝叶斯方法融合缺失的多模态临床数据，提升模型鲁棒性与可解释性。
Bayesian Integration of Nonlinear Incomplete Clinical Data
- 构建联合生成-判别潜变量框架，显式建模各模态和变量的缺失机制
- 在三个临床数据集上优于主流基线，尤其在数据不完整时表现更稳
- 通过潜空间分析揭示模态重要性，支持临床可解释洞察
多模态临床数据具有高维、异构表示和结构化缺失等特点，给预测建模、数据整合和可解释性带来挑战。我们提出BIONIC（贝叶斯非线性不完整临床数据集成）框架，通过联合生成-判别潜变量架构，在缺失条件下统一整合异构多模态数据。BIONIC利用医学图像和临床文本的预训练嵌入，将结构化临床变量直接纳入贝叶斯多模态建模中。该框架通过显式建模模态级和变量级缺失以及缺失标签，实现部分观测和半监督场景下的稳健学习。我们在三个多模态临床与生物医学数据集上评估BIONIC，结果表明其在多种不完整数据场景下均显著优于代表性基线，兼具强且一致的判别性能。除预测准确率外，其潜变量结构还提供内在可解释性，支持模态重要性的群体层面分析，有助于生成临床有意义的洞见。
原文摘要 · Abstract (English)
Multimodal clinical data are characterized by high dimensionality, heterogeneous representations, and structured missingness, posing significant challenges for predictive modeling, data integration, and interpretability. We propose BIONIC (Bayesian Integration of Nonlinear Incomplete Clinical data), a unified probabilistic framework that integrates heterogeneous multimodal data under missingness through a joint generative-discriminative latent architecture. BIONIC uses pretrained embeddings for complex modalities such as medical images and clinical text, while incorporating structured clinical variables directly within a Bayesian multimodal formulation. The proposed framework enables robust learning in partially observed and semi-supervised settings by explicitly modeling modality-level and variable-level missingness, as well as missing labels. We evaluate BIONIC on three multimodal clinical and biomedical datasets, demonstrating strong and consistent discriminative performance compared to representative multimodal baselines, particularly under incomplete data scenarios. Beyond predictive accuracy, BIONIC provides intrinsic interpretability through its latent structure, enabling population-level analysis of modality relevance and supporting clinically meaningful insight.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。