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| Main Author: | |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.15258 |
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| _version_ | 1866916535059087360 |
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| author | Faroz, Salman |
| author_facet | Faroz, Salman |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_15258 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DisEmbed: Transforming Disease Understanding through Embeddings Faroz, Salman Computation and Language Machine Learning 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. |
| title | DisEmbed: Transforming Disease Understanding through Embeddings |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2412.15258 |