Multimodal Clinical Trial Outcome Prediction with Large Language Models
<|reference_start|>Multimodal Clinical Trial Outcome Prediction with Large Language Models: 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.<|reference_end|>
@article{zheng2024multimodal,
    title={Multimodal Clinical Trial Outcome Prediction with Large Language Models},
    author={Wenhao Zheng, Dongsheng Peng, Hongxia Xu, Yun Li, Hongtu Zhu, Tianfan
  Fu, Huaxiu Yao},
    journal={arXiv preprint arXiv:2402.06512},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.06512},
    primaryClass={cs.LG cs.CL}
}