  The clinical trial is a pivotal and costly process, often spanning multiple
years and requiring substantial financial resources. Therefore, the development
of clinical trial outcome prediction models aims to exclude drugs likely to
fail and holds the potential for significant cost savings. Recent data-driven
attempts leverage deep learning methods to integrate multimodal data for
predicting clinical trial outcomes. However, these approaches rely on manually
designed modal-specific encoders, which limits both the extensibility to adapt
new modalities and the ability to discern similar information patterns across
different modalities. To address these issues, we propose a multimodal
mixture-of-experts (LIFTED) approach for clinical trial outcome prediction.
Specifically, LIFTED unifies different modality data by transforming them into
natural language descriptions. Then, LIFTED constructs unified noise-resilient
encoders to extract information from modal-specific language descriptions.
Subsequently, a sparse Mixture-of-Experts framework is employed to further
refine the representations, enabling LIFTED to identify similar information
patterns across different modalities and extract more consistent
representations from those patterns using the same expert model. Finally, a
mixture-of-experts module is further employed to dynamically integrate
different modality representations for prediction, which gives LIFTED the
ability to automatically weigh different modalities and pay more attention to
critical information. The experiments demonstrate that LIFTED significantly
enhances performance in predicting clinical trial outcomes across all three
phases compared to the best baseline, showcasing the effectiveness of our
proposed key components.
