Dataset:

Task Categories: question-answering
Languages: en zh
Multilinguality: monolingual
Size Categories: n>1K
Licenses: unknown
Language Creators: expert-generated found
Annotations Creators: found
Source Datasets: original

Dataset Card for [Dataset Name]

Dataset Summary

The MedDialog dataset (Chinese) contains conversations (in Chinese) between doctors and patients. It has 1.1 million dialogues and 4 million utterances. The data is continuously growing and more dialogues will be added. The raw dialogues are from haodf.com. All copyrights of the data belong to haodf.com.

The MedDialog dataset (English) contains conversations (in English) between doctors and patients. It has 0.26 million dialogues. The data is continuously growing and more dialogues will be added. The raw dialogues are from healthcaremagic.com and icliniq.com. All copyrights of the data belong to healthcaremagic.com and icliniq.com.

Directions for using the pre-trained model using BERT using PyTorch is available in the Homepage.

Supported Tasks and Leaderboards

Closed domain qa

Languages

Monolingual. The datasets are in English (EN) and Chinese (ZH)

Dataset Structure

Data Instances

For English:

Each consultation consists of the below:

  • ID
  • URL
  • Description of patient’s medical condition
  • Dialogue

The dataset is built from icliniq.com, healthcaremagic.com, healthtap.com and all copyrights of the data belong to these websites.

For Chinese:

Each consultation consists of the below:

  • ID
  • URL
  • Description of patient’s medical condition
  • Dialogue
  • (Optional) Diagnosis and suggestions.

The dataset is built from Haodf.com and all copyrights of the data belong to Haodf.com.

One example for chinese is

{
{'dialogue_id': 2,
  'dialogue_turns': [{'speaker': '病人',
    'utterance': '孩子哭闹时,鸡鸡旁边会肿起,情绪平静时肿块会消失,去一个私人诊所看过,说是疝气.如果确定是疝气,是不是一定要手术治疗?我孩子只有1岁10月,自愈的可能性大吗?如果一定要手术,这么小的孩子风险大吗?术后的恢复困难吗?谢谢.'},
   {'speaker': '医生', 'utterance': '南方医的B超说得不清楚,可能是鞘膜积液,可到我医院复查一个B超。'}],
  'dialogue_url': 'https://www.haodf.com/doctorteam/flow_team_6477251152.htm',
  'file_name': '2020.txt'},
}

Data Fields

For generating the QA only the below fields have been considered:

  • ID : Consultatation Identifier (restarts for each file)
  • URL: The url link of the extracted conversation
  • Dialogue : The conversation between the doctor and the patient.

These are arranged as below in the prepared dataset. Each item will be represented with these parameters.

  • "file_name": string - signifies the file from which the conversation was extracted
  • "dialogue_id": int32 - the dialogue id
  • "dialogue_url": string - url of the conversation
  • "dialogue_turns": datasets.Sequence - sequence of dialogues between patient and the doctor.Consists ClassLabel(names=["病人", "医生"]), and "utterance"(string) for each turn. (ClassLable(names=["Patient", "Doctor"]) for english)

Data Splits

There are no data splits on the original data

Dataset Creation

Curation Rationale

Medical dialogue systems are promising in assisting in telemedicine to increase access to healthcare services, improve the quality of patient care, and reduce medical costs.

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

Citation Information

@article{chen2020meddiag, title={MedDialog: a large-scale medical dialogue dataset}, author={Chen, Shu and Ju, Zeqian and Dong, Xiangyu and Fang, Hongchao and Wang, Sicheng and Yang, Yue and Zeng, Jiaqi and Zhang, Ruisi and Zhang, Ruoyu and Zhou, Meng and Zhu, Penghui and Xie, Pengtao}, journal={arXiv preprint arXiv:2004.03329}, year={2020} }

Contributions

Thanks to @vrindaprabhu for adding this dataset.

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