Bayesian Integration of Nonlinear Incomplete Clinical Data
This addresses the problem of predictive modeling and interpretability in clinical data integration for healthcare applications, representing an incremental improvement with a novel hybrid method.
The paper tackles the challenge of integrating high-dimensional, heterogeneous, and incomplete multimodal clinical data by proposing BIONIC, a Bayesian probabilistic framework, which demonstrated strong discriminative performance on three datasets, especially under incomplete data conditions.
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.