---
configs:
- config_name: default
license: apache-2.0
task_categories:
- question-answering
language:
- en
tags:
- medical
size_categories:
- 1K<n<10K
viewer: false
---

# RareBench [KDD2024 ADS Track]

**RareBench** is a pioneering benchmark designed to systematically evaluate the capabilities of LLMs on 4 critical dimensions within the realm of rare diseases.
Meanwhile, we have compiled the largest open-source dataset on rare disease patients, establishing a benchmark for future studies in this domain. To facilitate differential diagnosis of rare diseases, we develop a dynamic few-shot prompt methodology, leveraging a comprehensive rare disease knowledge graph synthesized from multiple knowledge bases, significantly enhancing LLMs’ diagnos-
tic performance. Moreover, we present an exhaustive comparative study of GPT-4’s diagnostic capabilities against those of specialist physicians. Our experimental findings underscore the promising potential of integrating LLMs into the clinical diagnostic process for rare diseases. 

Github Repo for RareBench: https://github.com/chenxz1111/RareBench 
Arxiv Paper for RareBench: https://arxiv.org/pdf/2402.06341.pdf



## How to use it?

#### Loading Data 

```python
from datasets import load_dataset

datasets = ["RAMEDIS", "MME", "HMS", "LIRICAL", "PUMCH_ADM"]

for dataset in datasets:
    data = load_dataset('chenxz/RareBench', dataset, split='test')
    print(data)
```

#### Data Format

```json
{
    "Phenotype": "The list of phenotypes presented in HPO codes",
    "RareDisease": "The list of rare diseases code including OMIM, Orphanet and CCRD format",
    "Department": "(Optional) Only provided in PUMCH_ADM"
}
```

#### Evaluation

This repository provides data and mapping files for **RareBench**. Please refer to our [github](https://github.com/chenxz1111/RareBench) for further automated evaluation.


## Source Data 

#### Data Collection and statistics

This study categorizes datasets into two main groups: publicly available datasets and the Peking Union Medical College Hospital (PUMCH) datasets.

| Dataset           | RAMEDIS | MME      |  HMS    | LIRICAL       | PUMCH_ADM |
| :---------------- | :------:| :------: |:------: | :-----------: |:--------: |
| Countries/Regions | Europe  | Canada   | Germany | Multi-Country | China     |
| \#Cases           | 624     | 40       | 88      | 370           | 75        |
| \#Disease         | 74     | 17       | 39      | 252           | 16        |
| \#Department      | N/A     | N/A      | N/A     | N/A           | 5         |
|\#Cases per disease|         |          |         |               |           |
|--- Minimum        | 1       | 1        | 1       | 1             |  3        |
|--- Median         | 2       | 1        | 1       | 1             |  5        |
|--- Maximum        |82       |11        |11       | 19            |  8        |
|\#HPO terms per case|        |          |         |               |           |
|--- Minimum        | 3       | 3        | 5       | 3             |  3        |
|--- Median         | 9       | 10.5     | 17.5    | 11            |  16       |
|--- Maximum        |46       |26        |54       | 95            |  47       |

> Note: The total number of cases in PUMCH is 1,650. We have currently only made public the 75 cases used in the Human versus LLMs experiment.

#### Data Processing

We apply reasonable filtering criteria to identify and remove cases of low quality that may be caused by recording errors or missing information, such as those with uncertain or imprecise diagnoses and those lacking sufficient relevant information, i.e., fewer than three phenotypes.

#### Personal and Sensitive Information

Doctors from PUMCH monitored all cases before uploading text information, ensuring the absence of any potential personal information leaks. 

#### Mapping Files

Files in mapping directory, including:

`phenotype_mapping.json`: HPO phenotype code mapping to term name

`disease_mapping.json`: OMIM/Orphanet/CCRD code mapping to disease name

`ic_dict.json`: HPO phenotype terms' Information Content(IC) values obtained from HPO hierarchical structure

`phe2embedding.json`: HPO phenotype terms' 256 dimension embedding vectors learned by IC-based random walk

## Citation
```
@article{chen2024rarebench,
  title={RareBench: Can LLMs Serve as Rare Diseases Specialists?},
  author={Chen, Xuanzhong and Mao, Xiaohao and Guo, Qihan and Wang, Lun and Zhang, Shuyang and Chen, Ting},
  journal={arXiv preprint arXiv:2402.06341},
  year={2024}
}
```