DeepImagine: Counterfactual Reasoning for Clinical Trial Prediction
DeepImagine serves as a framework designed to enhance the biomedical reasoning capabilities of large language models by utilizing successive counterfactual imagining. It simulates concealed causal mechanisms in clinical trials by training models to deduce how the results of trials would vary with controlled adjustments to factors like dosage, outcome measures, study arms, geography, and other trial characteristics. The framework generates both natural and approximate counterfactual pairs derived from actual clinical trials with documented outcomes. This method tackles the difficulty of forecasting future clinical trial results, an area where conventional correlational predictors and commercial LLMs have shown limited effectiveness.
Key facts
- DeepImagine is a framework for teaching LLMs biomedical reasoning.
- It uses successive counterfactual imagining.
- The central idea is to approximate hidden causal mechanisms of clinical trials.
- Models are trained to infer result changes under controlled perturbations.
- Perturbations include dosage, outcome measures, study arms, geography, and other trial attributes.
- Both natural and approximate counterfactual pairs are constructed from real clinical trials.
- Prior work shows limited performance of traditional predictors and commercial LLMs on this task.
- The research is published on arXiv with ID 2604.23054.
Entities
Institutions
- arXiv