task_categories:question-answering task_categories:summarization task_categories:text-generation task_categories:conversational size_categories:100K<n<1M language:en license:cc-by-nc-sa-4.0 medical arxiv:2309.00237 region:us
---
license: cc-by-nc-sa-4.0
task_categories:
- question-answering
- summarization
- text-generation
- conversational
language:
- en
tags:
- medical
pretty_name: 'Asclepius: Synthetic Clincal Notes & Instruction Dataset'
size_categories:
- 100K<n<1M
---
# Asclepius: Synthetic Clincal Notes & Instruction Dataset

## Dataset Description
- **Repository:**
  - [Github](https://github.com/starmpcc/Asclepius)
- **Paper:**
  - https://arxiv.org/abs/2309.00237
- **MODEL:**
  - https://huggingface.co/starmpcc/Asclepius-13B
  - https://huggingface.co/starmpcc/Asclepius-7B


### Dataset Summary

This dataset is official dataset for Asclepius [(arxiv)](https://arxiv.org/abs/2309.00237)
This dataset is composed with Clinical Note - Question - Answer format to build a clinical LLMs.
- We first synthesized synthetic notes from [PMC-Patients](https://huggingface.co/datasets/zhengyun21/PMC-Patients) case reports with GPT-3.5
- Then, we generate instruction-answer pairs for 157k synthetic discharge summaries

### Supported Tasks and Leaderboards

- This dataset covers below 8 tasks
  - Named Entity Recognition
  - Abbreviation Expansion
  - Relation Extraction
  - Temporal Information Extraction
  - Coreference Resolution
  - Paraphrasing
  - Summarization
  - Question Answering

### Languages

English

## Dataset Structure

### Data Instances

- `synthetic.csv`
  - Clinical Note - Question - Answer pairs

### Data Fields

- `patient_id`: Unique case report id from PMC-Patients
- `patient`: Case report text
- `question`: GPT-3.5 generated instruction from patient. The used prompt can be checked on github.
- `answer`: GPT-3.5 generated answer for given case report and question
- `task`: Corresponding category of question. One of above listsed


## Dataset Creation

### Source Data
[PMC-Patients](https://huggingface.co/datasets/zhengyun21/PMC-Patients)

### Annotations
We used GPT-3.5-turbo (version 0314).
You can check the prompts on our github.

## Additional Information

### Licensing Information

CC-BY-NC-SA 4.0

### Citation Information

@misc{kweon2023publicly,
    title={Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes},
    author={Sunjun Kweon and Junu Kim and Jiyoun Kim and Sujeong Im and Eunbyeol Cho and Seongsu Bae and Jungwoo Oh and Gyubok Lee and Jong Hak Moon and Seng Chan You and Seungjin Baek and Chang Hoon Han and Yoon Bin Jung and Yohan Jo and Edward Choi},
    year={2023},
    eprint={2309.00237},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}