MedNgage数据集
包含2100条癌症症状管理对话，标注社会情感与认知参与度，用于提升AI对医患对话的理解。
基本信息
资源简介
MedNgage数据集
【数据内容】
源站原始描述：MedNgage, a novel dataset consists of patient-nurse conversations on the asynchronized message boards about cancer symptom management - yamsanvich/MedNgage
【适用方向】
文本类数据可用于医学自然语言处理，如命名实体识别、信息抽取与文本分类。
【获取与许可】
源站页面：https://github.com/YanRayray/MedNgage
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取
git clone https://github.com/YanRayray/MedNgage.git
curl -L -o repo.zip https://github.com/YanRayray/MedNgage/archive/refs/heads/main.zip
unzip repo.zip
源站 README 摘录（使用方式）
MedNgage: A Dataset for Studying Engagement in Patient-Nurse Conversations
The MedNgage dataset contains de-identified patient-nurse conversations from asynchronized message boards on cancer symptom management. The dataset includes 2.1K turns between 4 nurse interventionists and 68 patients with recurrent ovarian cancer.
To ensure HIPAA compliance and protect patient privacy, we used the NLM Scrubber offered by NIH to produce the de-identified health information for scientific use. Two independent annotators evaluated the dataset to ensure that patients cannot be traced. The content provided in this work is licensed under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.
To access the dataset, interested investigators must comply with NIH data sharing requirements, including obtaining an Ethical Review from their institution and submitting a formal request for the data by filling out the Data Request Application Form. The form can be found in the “Forms/” directory.
Please send the completed Ethical Review and Data Request Application to Dr. Heidi Donovan (donovanh@pitt.edu;https://www.nursing.pitt.edu/person/heidi-donovan) to request access to the dataset.
数据加载示例（表格/文本类）
import pandas as pd, glob, os
files = (glob.glob(os.path.join(path, "**", "*.csv"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.tsv"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.xlsx"), recursive=True))
print("数据文件:", files)
df = pd.read_csv(files[0])
print(df.shape); print(df.columns.tolist()); print(df.head(3))
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
⚡️ 需要数据支持或标注服务？立即联系我们获取专业方案。
