# MARIA: a Multimodal Transformer Model for Incomplete Healthcare Data

arXiv: https://arxiv.org/abs/2412.14810 · HTML: https://mlsanity.com/paper/2412.14810/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 19 Dec 2024.

## Builds on (most cited first, 11 of 11)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [TabNet: Attentive Interpretable Tabular Learning](https://mlsanity.com/paper/1908.07442/) (2019)
- [TabTransformer: Tabular Data Modeling Using Contextual Embeddings](https://mlsanity.com/paper/2012.06678/) (2020)
- [SMIL: Multimodal Learning with Severely Missing Modality](https://mlsanity.com/paper/2103.05677/) (2021)
- [Multi-modal Learning with Missing Modality via Shared-Specific Feature Modelling](https://mlsanity.com/paper/2307.14126/) (2023)
- [M3Care: Learning with Missing Modalities in Multimodal Healthcare Data](https://mlsanity.com/paper/2210.17292/) (2022)
- [A Systematic Review of Intermediate Fusion in Multimodal Deep Learning for Biomedical Applications](https://mlsanity.com/paper/2408.02686/) (2024)
- [Benchmarking missing-values approaches for predictive models on health databases](https://mlsanity.com/paper/2202.10580/) (2022)
- [Cascaded Multi-Modal Mixing Transformers for Alzheimer's Disease Classification with Incomplete Data](https://mlsanity.com/paper/2210.00255/) (2022)
- [Not Another Imputation Method: A Transformer-based Model for Missing Values in Tabular Datasets](https://mlsanity.com/paper/2407.11540/) (2024)

## Built on by (most cited first, 5 of 5)

- [XGeM: A Multi-Prompt Foundation Model for Multimodal Medical Data Generation](https://mlsanity.com/paper/2501.04614/) (2025)
- [Beyond a Single Mode: GAN Ensembles for Diverse Medical Data Generation](https://mlsanity.com/paper/2503.24258/) (2025)
- [Missing-Modality-Aware Graph Neural Network for Cancer Classification](https://mlsanity.com/paper/2506.22901/) (2025)
- [Longitudinal NSCLC Treatment Progression via Multimodal Generative Models](https://mlsanity.com/paper/2603.06147/) (2026)
- [Learning from Limited and Incomplete Data: A Multimodal Framework for Predicting Pathological Response in NSCLC](https://mlsanity.com/paper/2603.15100/) (2026)

## Abstract (arXiv)

In healthcare, the integration of multimodal data is pivotal for developing comprehensive diagnostic and predictive models. However, managing missing data remains a significant challenge in real-world applications. We introduce MARIA (Multimodal Attention Resilient to Incomplete datA), a novel transformer-based deep learning model designed to address these challenges through an intermediate fusion strategy. Unlike conventional approaches that depend on imputation, MARIA utilizes a masked self-attention mechanism, which processes only the available data without generating synthetic values. This approach enables it to effectively handle incomplete datasets, enhancing robustness and minimizing biases introduced by imputation methods. We evaluated MARIA against 10 state-of-the-art machine learning and deep learning models across 8 diagnostic and prognostic tasks. The results demonstrate that MARIA outperforms existing methods in terms of performance and resilience to varying levels of data incompleteness, underscoring its potential for critical healthcare applications.