DrFuse: Learning Disentangled Representation for Clinical Multi-Modal
  Fusion with Missing Modality and Modal Inconsistency
Wenfang Yao Kejing Yin William K. Cheung Jia Liu Jing Qin
The combination of electronic health records (EHR) and medical images is
crucial for clinicians in making diagnoses and forecasting prognosis.
Strategically fusing these two data modalities has great potential to improve
the accuracy of machine learning models in clinical prediction tasks. However,
the asynchronous and complementary nature of EHR and medical images presents
unique challenges. Missing modalities due to clinical and administrative
factors are inevitable in practice, and the significance of each data modality
varies depending on the patient and the prediction target, resulting in
inconsistent predictions and suboptimal model performance. To address these
challenges, we propose DrFuse to achieve effective clinical multi-modal fusion.
It tackles the missing modality issue by disentangling the features shared
across modalities and those unique within each modality. Furthermore, we
address the modal inconsistency issue via a disease-wise attention layer that
produces the patient- and disease-wise weighting for each modality to make the
final prediction. We validate the proposed method using real-world large-scale
datasets, MIMIC-IV and MIMIC-CXR. Experimental results show that the proposed
method significantly outperforms the state-of-the-art models. Our
implementation is publicly available at https://github.com/dorothy-yao/drfuse.