MARIA用注意力机制直接处理缺失医疗数据，无需补全就能准确诊断。
MARIA: a Multimodal Transformer Model for Incomplete Healthcare Data
- 采用掩码自注意力机制，只处理实际可用数据
- 在8个任务中优于10种主流模型，对数据缺失更鲁棒
- 适合真实医疗场景中数据不全的诊断系统
在医疗领域，多模态数据融合对构建全面的诊断与预测模型至关重要。然而，现实应用中缺失数据仍是重大挑战。我们提出MARIA（Multimodal Attention Resilient to Incomplete datA），一种基于Transformer的深度学习模型，通过中间融合策略应对这一问题。不同于依赖插补的传统方法，MARIA采用掩码自注意力机制，仅处理可用数据，不生成合成值。该方法有效处理不完整数据集，提升模型鲁棒性并减少插补带来的偏差。我们在8个诊断与预后任务上，将MARIA与10种先进机器学习及深度学习模型进行对比。结果表明，MARIA在性能和对不同缺失水平的适应性方面均优于现有方法，凸显其在关键医疗应用中的潜力。
原文摘要 · Abstract (English)
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.
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