LeMoF: Level-guided Multimodal Fusion for Heterogeneous Clinical Data
This work addresses the challenge of fully exploiting modality-specific representations in clinical prediction for healthcare applications, representing an incremental improvement over existing fusion techniques.
The paper tackled the problem of multimodal clinical prediction by proposing LeMoF, a framework that integrates level-guided representations from heterogeneous data like EHR and biosignals, achieving consistent outperformance over state-of-the-art methods in length of stay prediction using ICU data.
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.