利用个人健康数据进行卵巢癌风险分层
基于NHIS和PLCO个人健康数据训练神经网络，预测并分层卵巢癌风险，识别可受益于靶向筛查的高危人群。
基本信息
资源简介
利用个人健康数据进行卵巢癌风险分层
【一句话概览】
本数据集是 figshare 公开的结构化表格数据集，收录 1 个文件、合计 103.18 KB，主题涵盖 ovarian cancer、neural network、risk stratification、personal health data、screening，可用于统计建模、特征工程与队列分析，CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《fdata-02-00024_Stratifying Ovarian Cancer Risk Using Personal Health Data.xml》。配套论文《Committee Opinion No. 716: The Role of the Obstetrician–Gynecologist in the Early Detection of Epithelial Ovarian Cancer in Women at Average Risk》发表于 Obstetrics & Gynecology（2017），DOI：10.1097/AOG.0000000000002299。源站首次发布：2020-03-06。
【数据内容】
源站原始描述：Purpose: Screening the general population for ovarian cancer is not recommended by every major medical or public health organization because the harms from screening outweigh the benefit it provides. To improve ovarian cancer detection and survival many are looking at high-risk populations who would benefit from screening. Methods: We train a neural network on readily available personal health data to predict and stratify ovarian cancer risk. We use two different datasets to train our network: T…
文件 fdata-02-00024_Stratifying Ovarian Cancer Risk Using Personal Health Data.xml 为文本内容，开头为：<?xml version="1.0" encoding="UTF-8" standalonevent-blocked="no"?> <!DOCTYPE article PUBLIC "-//NLM//DTD Journal Publishing DTD v2.3 20070202//EN" "journalpublishing.dtd"…
【数据结构与技术规格】
文件数 1 个，合计 103.18 KB。fdata-02-00024_Stratifying Ovarian Cancer Risk Using Personal Health Data.xml：undefined，103.18 KB。数据集 DOI：10.3389/fdata.2019.00024.s002。
【主题与分类】
源站学科分类：Data Structures、Artificial Intelligence and Image Processing、Computer Engineering、Applied Computer Science、Pattern Recognition and Data Mining。主题标签：ovarian cancer、neural network、risk stratification、personal health data、screening。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://frontiersin.figshare.com/articles/dataset/fdata-02-00024_Stratifying_Ovarian_Cancer_Risk_Using_Personal_Health_Data_xml/11948259
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Hart, Gregory R.; Nartowt, Bradley J.; Muhammad, Wazir; Liang, Ying; Huang, Gloria S.; Deng, Jun (2020). fdata-02-00024_Stratifying Ovarian Cancer Risk Using Personal Health Data.xml. Frontiers. Dataset
【使用注意】
· 源站最后修改于 2023-05-31，引用时请注意版本时效。
下载信息
注册下载
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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（Figshare）
- 源站标题：fdata-02-00024_Stratifying Ovarian Cancer Risk Using Personal Health Data.xml
- DOI：10.3389/fdata.2019.00024.s002
- 发布日期：2020-03-06
- 文件数：1 个
- 文件总体积：103.18 KB
- 源站页面：https://frontiersin.figshare.com/articles/dataset/fdata-02-00024_Stratifying_Ovarian_Cancer_Risk_Using_Personal_Health_Data_xml/11948259
命令行下载
curl -L -o "fdata-02-00024_Stratifying Ovarian Cancer Risk Using Personal Health Data.xml" "https://ndownloader.figshare.com/files/21932541"
文件清单
fdata-02-00024_Stratifying Ovarian Cancer Risk Using Personal Health Data.xml（103.18 KB）
文件总体积 103.18 KB（源站 API 实测）。
数据说明
Purpose: Screening the general population for ovarian cancer is not recommended by every major medical or public health organization because the harms from screening outweigh the benefit it provides. To improve ovarian cancer detection and survival many are looking at high-risk populations who would benefit from screening.
Methods: We train a neural network on readily available personal health data to predict and stratify ovarian cancer risk. We use two different datasets to train our network: The National Health Interview Survey and Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial.
Results: Our model has an area under the receiver operating characteristic curve of 0.71. We further demonstrate how the model could be used to stratify patients into different risk categories. A simple 3-tier scheme classifies 23.8% of those with cancer and 1.0% of those without as high-risk similar to genetic testing, and 1.1% of those with cancer and 24.4% of those without as low risk.
Conclusion: The developed neural network offers a cost-effective and non-invasive way to identify those who could benefit from targeted screening.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.3389/fdata.2019.00024.s002（source: https://frontiersin.figshare.com/articles/dataset/fdata-02-00024_Stratifying_Ovarian_Cancer_Risk_Using_Personal_Health_Data_xml/11948259）。
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。
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