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CancerGUIDE: Cancer Guideline Understanding via Internal Disagreement Estimation

This project aims to generate treatment predictions and clinical trajectories for patients with non-small cell lung cancer (NSCLC) using large language models (LLMs) applied to real-world clinical notes.

📁 Project Structure

1. `benchmark_generation`

Contains source code for generating consistency bench + synthetic benchmarks

2. `error_analysis`

Contains source code for generating analysis regarding zero-shot model failure mode predictions

3. `main_analysis`

Contains source code for generating model performance on cross-model consistency benchmark, general evaluation scripts, and ROC curve generation files.

4. `plot_generation`

Includes scripts for heatmap, bar chart, ROC curves, and confusion matrix analyses from main paper.

5. `bash`

Contains ordered bash scripts for executing code to validate paper performance. Ordered by analyses present in paper.