# The RareDis corpus: a corpus annotated with rare diseases, their signs and symptoms

arXiv: https://arxiv.org/abs/2108.01204 · HTML: https://mlsanity.com/paper/2108.01204/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 66 references that are in the map.
- First posted on arXiv 2 Aug 2021.

## Builds on (most cited first, 3 of 3)

- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://mlsanity.com/paper/2010.01057/) (2020)
- [CollaboNet: collaboration of deep neural networks for biomedical named entity recognition](https://mlsanity.com/paper/1809.07950/) (2018)

## Built on by (most cited first, 11 of 11)

- [Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT](https://mlsanity.com/paper/2205.09651/) (2022)
- [Identifying and Extracting Rare Disease Phenotypes with Large Language Models](https://mlsanity.com/paper/2306.12656/) (2023)
- [Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts](https://mlsanity.com/paper/2109.00343/) (2021)
- [Large Language Models Struggle in Token-Level Clinical Named Entity Recognition](https://mlsanity.com/paper/2407.00731/) (2024)
- [Evaluating Large Language Models for Public Health Classification and Extraction Tasks](https://mlsanity.com/paper/2405.14766/) (2024)
- [AutoRD: An Automatic and End-to-End System for Rare Disease Knowledge Graph Construction Based on Ontologies-enhanced Large Language Models](https://mlsanity.com/paper/2403.00953/) (2024)
- [Comparison of pipeline, sequence-to-sequence, and GPT models for end-to-end relation extraction: experiments with the rare disease use-case](https://mlsanity.com/paper/2311.13729/) (2023)
- [Decoding Rarity: Large Language Models in the Diagnosis of Rare Diseases](https://mlsanity.com/paper/2505.17065/) (2025)
- [RDMA: Cost Effective Agent-Driven Rare Disease Mining from Electronic Health Records](https://mlsanity.com/paper/2507.15867/) (2025)
- [Leveraging Large Language Models for Rare Disease Named Entity Recognition](https://mlsanity.com/paper/2508.09323/) (2025)
- [An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models](https://mlsanity.com/paper/2602.20324/) (2026)

## Abstract (arXiv)

The RareDis corpus contains more than 5,000 rare diseases and almost 6,000 clinical manifestations are annotated. Moreover, the Inter Annotator Agreement evaluation shows a relatively high agreement (F1-measure equal to 83.5% under exact match criteria for the entities and equal to 81.3% for the relations). Based on these results, this corpus is of high quality, supposing a significant step for the field since there is a scarcity of available corpus annotated with rare diseases. This could open the door to further NLP applications, which would facilitate the diagnosis and treatment of these rare diseases and, therefore, would improve dramatically the quality of life of these patients.