# DisEmbed: Transforming Disease Understanding through Embeddings

arXiv: https://arxiv.org/abs/2412.15258 · HTML: https://mlsanity.com/paper/2412.15258/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 11 references that are in the map.
- First posted on arXiv 16 Dec 2024.

## Builds on (most cited first, 6 of 6)

- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission](https://mlsanity.com/paper/1904.05342/) (2019)
- [Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets](https://mlsanity.com/paper/1906.05474/) (2019)
- [Efficient Natural Language Response Suggestion for Smart Reply](https://mlsanity.com/paper/1705.00652/) (2017)
- [MedCodER: A Generative AI Assistant for Medical Coding](https://mlsanity.com/paper/2409.15368/) (2024)

## Built on by (most cited first, 1 of 1)

- [On The Role of Pretrained Language Models in General-Purpose Text Embeddings: A Survey](https://mlsanity.com/paper/2507.20783/) (2025)

## Abstract (arXiv)

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.