relevanthint/scnclab2023

---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- machine-generated
license: []
multilinguality:
- monolingual
paperswithcode_id: scnclab2023
pretty_name: Synthetical Clinical Notes - Clab 2023
dataset_info:
features:
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0' : O
'1' : B-allergies
'2' : I-allergies
'3' : B-biomarkers
'4' : I-biomarkers
'5' : B-cancer_symptoms
'6' : I-cancer_symptoms
'7' : B-cancer_type
'8' : I-cancer_type
'9' : B-date
'10' : I-date
'11' : B-diagnosis
'12' : I-diagnosis
'13' : B-gender
'14' : I-gender
'15' : B-imaging_options
'16' : I-imaging_options
'17' : B-test_result
'18' : I-test_result
'19' : B-treatment
'20' : I-treatment
size_categories:
- n<1K
source_datasets:
- original
tags:
- bio
- clinic
- cancer
task_categories:
- token-classification
task_ids:
- named-entity-recognition
---

# Dataset Card for [scnclab2023]

## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)

## Dataset Description

- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [e-mail]

### Dataset Summary

[More Information Needed]

### Supported Tasks and Leaderboards

[More Information Needed]

### Languages

[More Information Needed]

## Dataset Structure

### Data Instances

[More Information Needed]

### Data Fields

[More Information Needed]

### Data Splits

[More Information Needed]

## Dataset Creation

### Curation Rationale

The Dataset has been created using the GPT-3 API by providing a prompt with some manually created clinical notes.

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

The annotation has been done using [Argilla](https://github.com/argilla-io)

#### Who are the annotators?

The sinthetical clinical notes have been annotated by a group of three biomedical experts

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

Note that this is not a real dataset.

### 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

[More Information Needed]

### Contributions

Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.

原始信息汇总

数据集概述

• 名称: Synthetical Clinical Notes - Clab 2023
• ID: scnclab2023
• 语言: 英语 (en)
• 语言生成方式: 机器生成
• 多语言性: 单语种
• 任务类别: 词元分类
• 任务ID: 命名实体识别
• 标签创建者: 专家生成
• 数据集大小: 小于1000条记录
• 源数据集: 原始数据
• 标签: 生物、临床、癌症

数据集结构

数据字段

• tokens: 字符串序列
• ner_tags: 标签序列，包含以下类别：
  • O
  • B-allergies
  • I-allergies
  • B-biomarkers
  • I-biomarkers
  • B-cancer_symptoms
  • I-cancer_symptoms
  • B-cancer_type
  • I-cancer_type
  • B-date
  • I-date
  • B-diagnosis
  • I-diagnosis
  • B-gender
  • I-gender
  • B-imaging_options
  • I-imaging_options
  • B-test_result
  • I-test_result
  • B-treatment
  • I-treatment

数据集创建

注释过程

• 注释工具: Argilla
• 注释者: 一组三名生物医学专家

数据生成

• 生成方式: 使用GPT-3 API，通过提供手动创建的临床笔记作为提示

注意事项

• 数据集真实性: 注意，这不是一个真实的数据集。