datasheet1_Q-Finder：临床数据分析中可信子组发现算法——应用于国际糖尿病管理实践研究.pdf

数据集获取（Figshare）

• 源站标题：datasheet1_Q-Finder: An Algorithm for Credible Subgroup Discovery in Clinical Data Analysis — An Application to the International Diabetes Management Practice Study.pdf
• DOI：10.3389/frai.2020.559927.s001
• 发布日期：2021-01-14
• 文件数：1 个
• 文件总体积：392.53 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/datasheet1_Q-Finder_An_Algorithm_for_Credible_Subgroup_Discovery_in_Clinical_Data_Analysis_An_Application_to_the_International_Diabetes_Management_Practice_Study_pdf/13574063

命令行下载

curl -L -o "datasheet1_Q-Finder: An Algorithm for Credible Subgroup Discovery in Clinical Data Analysis — An Application to the International Diabetes Management .pdf" "https://ndownloader.figshare.com/files/26050424"

文件清单

• datasheet1_Q-Finder: An Algorithm for Credible Subgroup Discovery in Clinical Data Analysis — An Application to the International Diabetes Management .pdf（392.53 KB）

文件总体积 392.53 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

Addressing the heterogeneity of both the outcome of a disease and the treatment response to an intervention is a mandatory pathway for regulatory approval of medicines. In randomized clinical trials (RCTs), confirmatory subgroup analyses focus on the assessment of drugs in predefined subgroups, while exploratory ones allow a posteriori the identification of subsets of patients who respond differently. Within the latter area, subgroup discovery (SD) data mining approach is widely used—particularly in precision medicine—to evaluate treatment effect across different groups of patients from various data sources (be it from clinical trials or real-world data). However, both the limited consideration by standard SD algorithms of recommended criteria to define credible subgroups and the lack of statistical power of the findings after correcting for multiple testing hinder the generation of hypothesis and their acceptance by healthcare authorities and practitioners. In this paper, we present the Q-Finder algorithm that aims to generate statistically credible subgroups to answer clinical questions, such as finding drivers of natural disease progression or treatment response. It combines an exhaustive search with a cascade of filters based on metrics assessing key credibility criteria, including relative risk reduction assessment, adjustment on confounding factors, individual feature’s contribution to the subgroup’s effect, interaction tests for assessing between-subgroup treatment effect interactions and tests adjustment (multiple testing). This allows Q-Finder to directly target and assess subgroups on recommended credibility criteria. The top-k credible subgroups are then selected, while accounting for subgroups’ diversity and, possibly, clinical relevance. Those subgroups are tested on independent data to assess their consistency across databases, while preserving statistical power by limiting the number of tests. To illustrate this algorithm, we applied it on the database of the International Diabetes Management Practice Study (IDMPS) to better understand the drivers of improved glycemic control and rate of episodes of hypoglycemia in type 2 diabetics patients. We co

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/frai.2020.559927.s001（source: https://frontiersin.figshare.com/articles/dataset/datasheet1_Q-Finder_An_Algorithm_for_Credible_Subgroup_Discovery_in_Clinical_Data_Analysis_An_Application_to_the_International_Diabetes_Management_Practice_Study_pdf/13574063）。