@inproceedings{zheng-etal-2025-lifted,
title = "{LIFTED}: Multimodal Clinical Trial Outcome Prediction via Large Language Models and Mixture-of-Experts",
author = "Zheng, Wenhao and
Wang, Liaoyaqi and
Peng, Dongshen and
Xu, Hongxia and
Li, Yun and
Zhu, Hongtu and
Fu, Tianfan and
Yao, Huaxiu",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.396/",
doi = "10.18653/v1/2025.findings-emnlp.396",
pages = "7503--7517",
ISBN = "979-8-89176-335-7",
abstract = "Clinical trials are pivotal yet costly processes, often spanning multiple years and requiring substantial expenses, motivating predictive models to identify likely-to-fail drugs early and save resources. Recent approaches leverage deep learning to integrate multimodal data for clinical outcome prediction; however, they rely heavily on manually designed modality-specific encoders, limiting their adaptability to new modalities and ability to effectively share information across modalities. To address these challenges, we propose a multimodal mixture-of-experts (LIFTED) framework. Specifically, LIFTED transforms modality-specific data into natural language descriptions, encoded via unified, noise-resilient encoders. A sparse Mixture-of-Experts mechanism then identifies shared patterns across modalities, extracting consistent representations. Finally, another mixture-of-experts module dynamically integrates these modality representations, emphasizing critical information. Experiments show that LIFTED significantly outperforms baseline methods in predicting clinical trial outcomes across all phases, highlighting the effectiveness of our proposed approach."
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<abstract>Clinical trials are pivotal yet costly processes, often spanning multiple years and requiring substantial expenses, motivating predictive models to identify likely-to-fail drugs early and save resources. Recent approaches leverage deep learning to integrate multimodal data for clinical outcome prediction; however, they rely heavily on manually designed modality-specific encoders, limiting their adaptability to new modalities and ability to effectively share information across modalities. To address these challenges, we propose a multimodal mixture-of-experts (LIFTED) framework. Specifically, LIFTED transforms modality-specific data into natural language descriptions, encoded via unified, noise-resilient encoders. A sparse Mixture-of-Experts mechanism then identifies shared patterns across modalities, extracting consistent representations. Finally, another mixture-of-experts module dynamically integrates these modality representations, emphasizing critical information. Experiments show that LIFTED significantly outperforms baseline methods in predicting clinical trial outcomes across all phases, highlighting the effectiveness of our proposed approach.</abstract>
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%0 Conference Proceedings
%T LIFTED: Multimodal Clinical Trial Outcome Prediction via Large Language Models and Mixture-of-Experts
%A Zheng, Wenhao
%A Wang, Liaoyaqi
%A Peng, Dongshen
%A Xu, Hongxia
%A Li, Yun
%A Zhu, Hongtu
%A Fu, Tianfan
%A Yao, Huaxiu
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F zheng-etal-2025-lifted
%X Clinical trials are pivotal yet costly processes, often spanning multiple years and requiring substantial expenses, motivating predictive models to identify likely-to-fail drugs early and save resources. Recent approaches leverage deep learning to integrate multimodal data for clinical outcome prediction; however, they rely heavily on manually designed modality-specific encoders, limiting their adaptability to new modalities and ability to effectively share information across modalities. To address these challenges, we propose a multimodal mixture-of-experts (LIFTED) framework. Specifically, LIFTED transforms modality-specific data into natural language descriptions, encoded via unified, noise-resilient encoders. A sparse Mixture-of-Experts mechanism then identifies shared patterns across modalities, extracting consistent representations. Finally, another mixture-of-experts module dynamically integrates these modality representations, emphasizing critical information. Experiments show that LIFTED significantly outperforms baseline methods in predicting clinical trial outcomes across all phases, highlighting the effectiveness of our proposed approach.
%R 10.18653/v1/2025.findings-emnlp.396
%U https://aclanthology.org/2025.findings-emnlp.396/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.396
%P 7503-7517
Markdown (Informal)
[LIFTED: Multimodal Clinical Trial Outcome Prediction via Large Language Models and Mixture-of-Experts](https://aclanthology.org/2025.findings-emnlp.396/) (Zheng et al., Findings 2025)
ACL
- Wenhao Zheng, Liaoyaqi Wang, Dongshen Peng, Hongxia Xu, Yun Li, Hongtu Zhu, Tianfan Fu, and Huaxiu Yao. 2025. LIFTED: Multimodal Clinical Trial Outcome Prediction via Large Language Models and Mixture-of-Experts. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 7503–7517, Suzhou, China. Association for Computational Linguistics.