DisEmbed: Transforming Disease Understanding through Embeddings

• URL: http://arxiv.org/abs/2412.15258v1
• Date: Mon, 16 Dec 2024 12:04:22 GMT
• Title: DisEmbed: Transforming Disease Understanding through Embeddings
• Authors: Salman Faroz,
• Abstract summary: DisEmbed is a disease-focused embedding model. DisEmbed is trained on a synthetic dataset curated to include disease descriptions, symptoms, and disease-related Q&A pairs.
• Score: 0.0
• License:
• 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.

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