Optimizing Rare Disease Patient Matching with Large Language Models

We present RepLLaMA, a neural ranking model for optimizing patient matching in rare disease communities. Using data from Unrare.me consisting of over two thousand profiles and over ten thousand ratings, our bi-encoder architecture maps profiles to 4096-dimensional vectors, enabling efficient similarity computations. The system processes unstructured symptom descriptions and structured responses, incorporating expert-guided LLM enhancements. Results show Top-10 Recall of 49.36% (±2.03), surpassing baselines while maintaining generalization. The implementation provides a scalable solution for rare disease patient matching, addressing computational complexity challenges.