# Bayesian Integration of Nonlinear Incomplete Clinical Data

arXiv: https://arxiv.org/abs/2602.01924 · HTML: https://mlsanity.com/paper/2602.01924/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 43 references that are in the map.
- First posted on arXiv 2 Feb 2026.

## Builds on (most cited first, 25 of 25)

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- [MMIST-ccRCC: A Real World Medical Dataset for the Development of Multi-Modal Systems](https://mlsanity.com/paper/2405.01658/) (2024)
- [Sparse Semi-supervised Heterogeneous Interbattery Bayesian Analysis](https://mlsanity.com/paper/2001.08975/) (2020)
- [Automated Ensemble Multimodal Machine Learning for Healthcare](https://mlsanity.com/paper/2407.18227/) (2024)
- [Interpretable Generative and Discriminative Learning for Multimodal and Incomplete Clinical Data](https://mlsanity.com/paper/2510.09513/) (2025)
- [Unified Bayesian representation for high-dimensional multi-modal biomedical data for small-sample classification](https://mlsanity.com/paper/2411.07043/) (2024)

## Abstract (arXiv)

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.