模型信息
包含丹麦2型糖尿病患者临床、医疗和社会人口学数据的表格数据集，用于预测最佳血糖监测间隔。
基本信息
资源简介
模型信息
【一句话概览】
针对慢病监测“一刀切”可能造成支持过度或不足，本研究利用丹麦2015—2018年T2D患者行政与临床数据（样本逾5.7万），以逻辑回归、随机森林和XGBoost预测HbA1c最佳检测间隔（3、6、9、12个月）。XGBoost表现最佳，并用SHAP解释特征重要性。模型AUC为0.53—0.89，在收入和教育层面公平性较好，但对控制良好者延长间隔可释放资源，同时存在假阴性风险。研究展示ML辅助个性化糖尿病管理、按检测频率分类患者的潜力，仍需多人群（来源 figshare，CC BY 4.0 许可）。
【数据集背景】
本数据集源站标题为《Model information.》。源站首次发布：2025-02-13。
【数据内容】
源站原始描述：Background Chronic disease monitoring programs often adopt a one-size-fits-all approach that does not consider variation in need, potentially leading to excessive or insufficient support for patients at different risk levels. Machine learning (ML) developments offer new opportunities for personalised medicine in clinical practice. Objective To demonstrate the potential of ML to guide resource allocation and tailored disease management, this study aims to predict the optimal testing interval for…
【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.pone.0317722.t002。
【主题与分类】
源站学科分类：Medicine、Biotechnology、Mathematical Sciences not elsewhere classified、Cancer、Infectious Diseases。主题标签：type 2 diabetes、tailored disease management、monitoring blood glucose、guide resource allocation、extreme gradient boosting、evaluated using roc、diverse patient populations、different durations associated、predicting longer intervals、national clinical databases。
【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://plos.figshare.com/articles/dataset/Model_information_/28410706
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Pedersen, Sasja Maria; Damslund, Nicolai; Kjær, Trine; Olsen, Kim Rose (2025). Model information.. PLOS ONE. Dataset
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：Model information.
- DOI：10.1371/journal.pone.0317722.t002
- 发布日期：2025-02-13
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Model_information_/28410706
命令行下载
curl -L -o "Table 2.xls" "https://ndownloader.figshare.com/files/52346536"
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
Background
Chronic disease monitoring programs often adopt a one-size-fits-all approach that does not consider variation in need, potentially leading to excessive or insufficient support for patients at different risk levels. Machine learning (ML) developments offer new opportunities for personalised medicine in clinical practice.
Objective
To demonstrate the potential of ML to guide resource allocation and tailored disease management, this study aims to predict the optimal testing interval for monitoring blood glucose (HbA1c) for patients with Type 2 Diabetes (T2D). We examine fairness across income and education levels and evaluate the risk of false-positives and false-negatives.
Data
Danish administrative registers are linked with national clinical databases. Our population consists of all T2D patients from 2015-2018, a sample of more than 57,000. Data contains patient-level clinical measures, healthcare utilisation, medicine, and socio-demographics.
Methods
We classify HbA1c test intervals into four categories (3, 6, 9, and 12 months) using three classification algorithms: logistic regression, random forest, and extreme gradient boosting (XGBoost). Feature importance is assessed with SHAP model explanations on the best-performing model, which was XGBoost. A training set comprising 80% of the data is used to predict optimal test intervals, with 20% reserved for testing. Cross-validation is employed to enhance the model’s reliability and reduce overfitting. Model performance is evaluated using ROC-AUC, and optimal intervals are determined based on a “time-to-next-positive-test” concept, with different durations associated with specific intervals.
Results
The model exhibits varying predictive accuracy, with AUC scores ranging from 0.53 to 0.89 across different test intervals. We find significant potential to free resources by prolonging the test interval for well-controlled patients. The fairness metric suggests models perform well in terms of equality. There is a sizeable risk of false negatives (predicting longer intervals than optimal), which requires attention.
Conclusions
We demonstrate the potential to use ML in personalised diabetes management by assisti
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0317722.t002（source: https://plos.figshare.com/articles/dataset/Model_information_/28410706）。
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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