基于预算约束的强化风险预测：使用电子健康记录中的不规则测量数据

数据集获取（Figshare）

• 源站标题：Reinforced Risk Prediction With Budget Constraint Using Irregularly Measured Data From Electronic Health Records
• DOI：10.6084/m9.figshare.16595973.v2
• 发布日期：2021-11-30
• 文件数：3 个
• 文件总体积：2.11 MB
• 源站页面：https://tandf.figshare.com/articles/dataset/Reinforced_risk_prediction_with_budget_constraint_using_irregularly_measured_data_from_electronic_health_records/16595973

命令行下载

curl -L -o "README.txt" "https://ndownloader.figshare.com/files/31623431"
curl -L -o "code.zip" "https://ndownloader.figshare.com/files/31623434"
curl -L -o "reinforced_supp.pdf" "https://ndownloader.figshare.com/files/31623437"

文件清单

• README.txt（112 B）
• code.zip（12.94 KB）
• reinforced_supp.pdf（2.10 MB）

文件总体积 2.11 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

Uncontrolled glycated hemoglobin (HbA1c) levels are associated with adverse events among complex diabetic patients. These adverse events present serious health risks to affected patients and are associated with significant financial costs. Thus, a high-quality predictive model that could identify high-risk patients so as to inform preventative treatment has the potential to improve patient outcomes while reducing healthcare costs. Because the biomarker information needed to predict risk is costly and burdensome, it is desirable that such a model collect only as much information as is needed on each patient so as to render an accurate prediction. We propose a sequential predictive model that uses accumulating patient longitudinal data to classify patients as: high-risk, low-risk, or uncertain. Patients classified as high-risk are then recommended to receive preventative treatment and those classified as low-risk are recommended to standard care. Patients classified as uncertain are monitored until a high-risk or low-risk determination is made. We construct the model using claims and enrollment files from Medicare, linked with patient electronic health records (EHR) data. The proposed model uses functional principal components to accommodate noisy longitudinal data and weighting to deal with missingness and sampling bias. The proposed method demonstrates higher predictive accuracy and lower cost than competing methods in a series of simulation experiments and application to data on complex patients with diabetes. Supplementary materials for this article are available online.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.16595973.v2（source: https://tandf.figshare.com/articles/dataset/Reinforced_risk_prediction_with_budget_constraint_using_irregularly_measured_data_from_electronic_health_records/16595973）。