通过分层引导融合，提升多模态临床数据预测稳定性与判别力。
LeMoF: Level-guided Multimodal Fusion for Heterogeneous Clinical Data
- 按编码器层级分离各模态表示，分层选择性融合。
- 在ICU数据上预测住院时长，性能优于现有方法。
- 适合处理异构临床数据的复杂预测任务。
多模态临床预测广泛用于整合电子健康记录（EHR）和生物信号等异构数据。然而，现有方法通常依赖静态模态融合策略，难以充分挖掘模态特异性表示。本文提出分层引导多模态融合（LeMoF），通过在各模态内选择性整合不同编码层提取的层次化表示，显式分离全局模态级预测与层级特异性判别表示。该设计使模型在异构临床环境中兼具预测稳定性和判别能力。在重症监护室（ICU）数据上的住院时长预测实验表明，LeMoF在多种编码器配置下均持续优于现有先进多模态融合技术。同时验证了分层融合是实现跨多种临床条件稳健预测的关键因素。
原文摘要 · Abstract (English)
Multimodal clinical prediction is widely used to integrate heterogeneous data such as Electronic Health Records (EHR) and biosignals. However, existing methods tend to rely on static modality integration schemes and simple fusion strategies. As a result, they fail to fully exploit modality-specific representations. In this paper, we propose Level-guided Modal Fusion (LeMoF), a novel framework that selectively integrates level-guided representations within each modality. Each level refers to a representation extracted from a different layer of the encoder. LeMoF explicitly separates and learns global modality-level predictions from level-specific discriminative representations. This design enables LeMoF to achieve a balanced performance between prediction stability and discriminative capability even in heterogeneous clinical environments. Experiments on length of stay prediction using Intensive Care Unit (ICU) data demonstrate that LeMoF consistently outperforms existing state-of-the-art multimodal fusion techniques across various encoder configurations. We also confirmed that level-wise integration is a key factor in achieving robust predictive performance across various clinical conditions.
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