n2c2 2008肥胖数据集

Name: n2c2 2008肥胖数据集
Creator: bigbio

包含肥胖和糖尿病患者出院摘要的文本分类数据集，由专家标注疾病状态。

bigbio

Hugging Face

2022-12-22 更新

文本肥胖文本分类

基本信息

模态

文本

大小

0.01 GB

创建/更新时间

2022-12-22

资源简介

该数据集包含来自Partners HealthCare研究患者数据仓库的出院摘要，选自因肥胖或糖尿病住院的超重或糖尿病患者的记录。数据经过半自动去标识化处理，并由两位肥胖专家根据出院摘要中的明确信息对疾病进行分类（存在、不存在、可疑、未提及）。主要应用于文本分类任务。

原始链接

https://huggingface.co/datasets/bigbio/n2c2_2008

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使用方式

数据集获取与加载（Hugging Face datasets）

# 前置依赖: pip install datasets
from datasets import load_dataset

ds = load_dataset("bigbio/n2c2_2008")
print(ds)  # 查看 splits 与字段结构
# 国内网络可先设镜像: import os; os.environ["HF_ENDPOINT"]="https://hf-mirror.com"

git lfs install &amp;&amp; git clone https://hf-mirror.com/datasets/bigbio/n2c2_2008

仓库文件结构

.gitattributes
README.md
bigbiohub.py
n2c2_2008.py

数据集卡片摘录（源站说明）

language:

en
bigbio_language:
English
license: other
multilinguality: monolingual
bigbio_license_shortname: DUA
pretty_name: n2c2 2008 Obesity
homepage: https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/
bigbio_pubmed: True
bigbio_public: False
bigbio_tasks:
TEXT_CLASSIFICATION

Dataset Card for n2c2 2008 Obesity

Dataset Description

Homepage: https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/
Pubmed: True
Public: False
Tasks: TXTCLASS

The data for the n2c2 2008 obesity challenge consisted of discharge summaries from
the Partners HealthCare Research Patient Data Repository. These data were chosen
from the discharge summaries of patients who were overweight or diabetic and had
been hospitalized for obesity or diabetes sometime since 12/1/04. De-identification
was performed semi-automatically. All private health information was replaced with
synthetic identifiers.

The data for the challenge were annotated by two obesity experts from the
Massachusetts General Hospital Weight Center. The experts were given a textual task,
which asked them to classify each disease (see list of diseases above) as Present,
Absent, Questionable, or Unmentioned based on explicitly documented information in
the discharge summaries, e.g., the statement “the patient is obese”. The experts were
also given an intuitive task, which asked them to classify each disease as Present,
Absent, or Questionable by applying their intuition and judgment to information in
the discharge summaries.

Citation Information

@article{uzuner2009recognizing,
    author = {
        Uzuner, Ozlem
    },
    title     = {Recognizing Obesity and Comorbidities in Sparse Data},
    journal   = {Journal of the American Medical Informatics Association},
    volume    = {16},
    number    = {4},
    pages     = {561-570},
    year      = {2009},
    monevent-blocked= {07},
    url       = {https://doi.org/10.1197/jamia.M3115},
    doi       = {10.1197/jamia.M3115},
    eprint    = {https://academic.oup.com/jamia/article-pdf/16/4/561/2302602/16-4-561.pdf}
}