数据统计
基于加拿大初级保健网络的纵向EMR数据集，包含19000+患者记录，用于2型糖尿病的早期预测。
基本信息
资源简介
数据统计
【一句话概览】
本数据集是 figshare 公开的时序数据集，收录 1 个文件、合计 5.63 KB，主题涵盖 proposed research investigates、modern preventable pandemic、fasting blood sugar、excellent available treatments、empirical findings demonstrate，可用于疾病进展预测与生理信号时序建模，CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《Statics of data.》。源站首次发布：2023-10-25。
【数据内容】
源站原始描述：Diabetes mellitus type 2 is increasingly being called a modern preventable pandemic, as even with excellent available treatments, the rate of complications of diabetes is rapidly increasing. Predicting diabetes and identifying it in its early stages could make it easier to prevent, allowing enough time to implement therapies before it gets out of control. Leveraging longitudinal electronic medical record (EMR) data with deep learning has great potential for diabetes prediction. This paper examin…
中文译文（机器翻译，仅供快速理解，请以原文为准）：2型糖尿病正越来越被称为一种现代可预防的大流行病，因为即使已有很好的治疗方法，糖尿病并发症的发生率仍在迅速上升。预测糖尿病并在其早期阶段识别它，可能使其更易预防
【数据结构与技术规格】
文件数 1 个，合计 5.63 KB。Table 2.xls：application/vnd.ms-excel，5.63 KB。数据集 DOI：10.1371/journal.pdig.0000354.t002。
【主题与分类】
源站学科分类：Chemical Sciences not elsewhere classified、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Science Policy。主题标签：proposed research investigates、modern preventable pandemic、fasting blood sugar、excellent available treatments、empirical findings demonstrate、body mass index、allowing enough time、middle aged patients、19k patients extracted、training data density。
【适用方向】
时序数据适用于疾病进展预测、生理信号监测与事件发生的时序建模。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/Statics_of_data_/24436484
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Naveed, Iqra; Kaleem, Muhammad Farhat; Keshavjee, Karim; Guergachi, Aziz (2023). Statics of data.. PLOS Digital Health. Dataset
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（Figshare）
- 源站标题：Statics of data.
- DOI：10.1371/journal.pdig.0000354.t002
- 发布日期：2023-10-25
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Statics_of_data_/24436484
命令行下载
curl -L -o "Table 2.xls" "https://ndownloader.figshare.com/files/42899489"
文件清单
Table 2.xls（5.63 KB）
文件总体积 5.63 KB（源站 API 实测）。
数据说明
Diabetes mellitus type 2 is increasingly being called a modern preventable pandemic, as even with excellent available treatments, the rate of complications of diabetes is rapidly increasing. Predicting diabetes and identifying it in its early stages could make it easier to prevent, allowing enough time to implement therapies before it gets out of control. Leveraging longitudinal electronic medical record (EMR) data with deep learning has great potential for diabetes prediction. This paper examines the predictive competency of deep learning models in contrast to state-of-the-art machine learning models to incorporate the time dimension of risk. The proposed research investigates a variety of deep learning models and features for predicting diabetes. Model performance was appraised and compared in relation to predominant features, risk factors, training data density and visit history. The framework was implemented on the longitudinal EMR records of over 19K patients extracted from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). Empirical findings demonstrate that deep learning models consistently outperform other state-of-the-art competitors with prediction accuracy of above 91%, without overfitting. Fasting blood sugar, hemoglobin A1c and body mass index are the key predictors of future onset of diabetes. Overweight, middle aged patients and patients with hypertension are more vulnerable to developing diabetes, consistent with what is already known. Model performance improves as training data density or the visit history of a patient increases. This study confirms the ability of the LSTM deep learning model to incorporate the time dimension of risk in its predictive capabilities.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pdig.0000354.t002（source: https://plos.figshare.com/articles/dataset/Statics_of_data_/24436484）。
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。
