DisEmbed: Transforming Disease Understanding through Embeddings

TL;DR

DisEmbed is a disease-focused embedding model trained on a synthetic dataset, outperforming general medical models in disease-specific tasks and retrieval-augmented generation applications.

Contribution

The paper introduces DisEmbed, a novel disease-specific embedding model trained on curated synthetic data, enhancing disease understanding and differentiation.

Findings

DisEmbed outperforms existing models on disease-specific benchmarks.

It excels in identifying disease contexts and differentiating similar diseases.

DisEmbed shows robustness in retrieval-augmented generation tasks.

Abstract

The medical domain is vast and diverse, with many existing embedding models focused on general healthcare applications. However, these models often struggle to capture a deep understanding of diseases due to their broad generalization across the entire medical field. To address this gap, I present DisEmbed, a disease-focused embedding model. DisEmbed is trained on a synthetic dataset specifically curated to include disease descriptions, symptoms, and disease-related Q\&A pairs, making it uniquely suited for disease-related tasks. For evaluation, I benchmarked DisEmbed against existing medical models using disease-specific datasets and the triplet evaluation method. My results demonstrate that DisEmbed outperforms other models, particularly in identifying disease-related contexts and distinguishing between similar diseases. This makes DisEmbed highly valuable for disease-specific use cases, including retrieval-augmented generation (RAG) tasks, where its performance is particularly robust.