AutoCT: Automating Interpretable Clinical Trial Prediction with LLM Agents (2025.emnlp-main)
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| Challenge: | Clinical trials are expensive and time-consuming, and accurate trial prediction is key to advancing medical treatments. |
| Approach: | They propose a framework that combines reasoning capabilities of large language models with the explainability of classical machine learning to generate, evaluate, and refine tabular features without human input. |
| Outcome: | The proposed framework performs better than SOTA methods on clinical trial prediction tasks within a limited number of iterations. |
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