预测变量

数据集获取（Figshare）

• 源站标题：Predictor variables.
• DOI：10.1371/journal.pone.0317722.t001
• 发布日期：2025-02-13
• 文件数：1 个
• 文件总体积：9.73 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Predictor_variables_/28410703

命令行下载

curl -L -o "Table 1.xls" "https://ndownloader.figshare.com/files/52346533"

文件清单

• Table 1.xls（9.73 KB）

文件总体积 9.73 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

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.t001（source: https://plos.figshare.com/articles/dataset/Predictor_variables_/28410703）。