DisEmbed: Transforming Disease Understanding through Embeddings
This work addresses the need for better disease understanding in medical AI applications, but it is incremental as it focuses on a specific domain within healthcare.
The paper tackles the problem of existing medical embedding models struggling with deep disease understanding by introducing DisEmbed, a disease-focused model trained on a synthetic dataset of disease descriptions, symptoms, and Q&A pairs, which outperforms other models in disease-specific tasks, such as identifying disease contexts and distinguishing similar diseases.
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.