利用纵向健康信息学数据预测复发性事件数据集

数据集获取（Figshare）

• 源站标题：Global and Episode-Specific Prediction of Recurrent Events Using Longitudinal Health Informatics Data
• DOI：10.6084/m9.figshare.28946244.v1
• 发布日期：2025-05-07
• 文件数：2 个
• 文件总体积：1.02 MB
• 源站页面：https://tandf.figshare.com/articles/dataset/Global_and_Episode-Specific_Prediction_of_Recurrent_Events_Using_Longitudinal_Health_Informatics_Data/28946244

命令行下载

curl -L -o "online_supp.pdf" "https://ndownloader.figshare.com/files/54274389"
curl -L -o "uasa_a_2497569_sm1018.pdf" "https://ndownloader.figshare.com/files/54274392"

文件清单

• online_supp.pdf（864.25 KB）
• uasa_a_2497569_sm1018.pdf（152.28 KB）

文件总体积 1.02 MB（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

Accurate prediction of recurrent clinical events is crucial for effective management of chronic conditions such as cancer and cardiovascular disease. In recent years, longitudinal health informatics databases, which routinely collect data on repeated clinical events, have been increasingly used to construct risk prediction models. We introduce a novel nonparametric framework to predict recurrent events on a gap time scale using survival tree ensembles. Our framework incorporates two predictive modeling strategies: episode-specific model and global model. These models avoid strong assumptions on how future event risk depends on previous event history and other predictors, making them a promising alternative to Cox-type models. Additional complexities in tree-based prediction for recurrent events include induced informative censoring of gap times and inter-event correlations. We develop algorithms to address these issues through the use of inverse probability of censoring weighting and modified resampling procedures. Applied to SEER-Medicare data to predict repeated hospitalizations for breast cancer patients, our models showed superior performance. In particular, borrowing information across events via global models substantially improved prediction accuracy for later hospitalizations. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.28946244.v1（source: https://tandf.figshare.com/articles/dataset/Global_and_Episode-Specific_Prediction_of_Recurrent_Events_Using_Longitudinal_Health_Informatics_Data/28946244）。