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 && 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}
}

完整数据卡：huggingface.co/datasets/bigbio/n2c2_2008