Summary of Multimodal Clinical Trial Outcome Prediction with Large Language Models, by Wenhao Zheng et al.
Multimodal Clinical Trial Outcome Prediction with Large Language Models
by Wenhao Zheng, Liaoyaqi Wang, Dongshen Peng, Hongxia Xu, Yun Li, Hongtu Zhu, Tianfan Fu, Huaxiu Yao
First submitted to arxiv on: 9 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach called LIFTED for predicting clinical trial outcomes using multimodal data. Traditional methods rely on manual design of modal-specific encoders, which limits their adaptability and ability to identify similar information patterns across different modalities. LIFTED addresses these limitations by transforming modal-specific data into natural language descriptions, then constructing unified noise-resilient encoders to extract relevant information. A sparse Mixture-of-Experts framework is employed to refine the representations, enabling LIFTED to identify consistent patterns and extract more accurate information. The final module dynamically integrates modality representations for prediction, allowing LIFTED to weigh different modalities and focus on critical information. Experimental results demonstrate that LIFTED outperforms baseline models in predicting clinical trial outcomes across all three phases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to predict how well a medicine will work in clinical trials. Right now, it takes a long time and costs a lot of money to test medicines this way. The new approach uses special computer programs that can look at different types of data all at once, like lab results and patient information. This helps the program make better predictions about which medicines are most likely to work. The program is called LIFTED, and it does a much better job than other methods at predicting the outcomes of clinical trials. This could save time and money in the long run. |
Keywords
* Artificial intelligence * Mixture of experts
