MARIA: A multimodal transformer model for incomplete healthcare data.

Journal: Computers in biology and medicine
Published Date: Aug 9, 2025

Abstract

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 modified 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. To support transparency and encourage further research, the source code is openly available at https://github.com/cosbidev/MARIA.

Authors

  • Camillo Maria Caruso
    Research Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy. Electronic address: camillomaria.caruso@unicampus.it.
  • Paolo Soda
    Unit of Computer Systems and Bioinformatics, Department of Engineering, University Campus Bio-Medico of Rome, Italy; Department of Radiation Sciences, Radiation Physics, Biomedical Engineering, Umeå, University, Umeå, Sweden. Electronic address: paolo.soda@umu.se.
  • Valerio Guarrasi
    Unit of Computer Systems and Bioinformatics, Department of Engineering, University Campus Bio-Medico of Rome, Italy; Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Italy. Electronic address: valerio.guarrasi@unicampus.it.

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