匹配学习优化个体化治疗策略（基于电子健康记录）

数据集获取（Figshare）

• 源站标题：Matched Learning for Optimizing Individualized Treatment Strategies Using Electronic Health Records
• DOI：10.6084/m9.figshare.7461749.v2
• 发布日期：2021-09-29
• 文件数：1 个
• 文件总体积：579.39 KB
• 源站页面：https://tandf.figshare.com/articles/dataset/Matched_Learning_for_Optimizing_Individualized_Treatment_Strategies_Using_Electronic_Health_Records/7461749

命令行下载

curl -L -o "uasa_a_1549050_sm6205.zip" "https://ndownloader.figshare.com/files/30925411"

文件清单

• uasa_a_1549050_sm6205.zip（579.39 KB）

文件总体积 579.39 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

Current guidelines for treatment decision making largely rely on data from randomized controlled trials (RCTs) studying average treatment effects. They may be inadequate to make individualized treatment decisions in real-world settings. Large-scale electronic health records (EHR) provide opportunities to fulfill the goals of personalized medicine and learn individualized treatment rules (ITRs) depending on patient-specific characteristics from real-world patient data. In this work, we tackle challenges with EHRs and propose a machine learning approach based on matching (M-learning) to estimate optimal ITRs from EHRs. This new learning method performs matching instead of inverse probability weighting as commonly used in many existing methods for estimating ITRs to more accurately assess individuals’ treatment responses to alternative treatments and alleviate confounding. Matching-based value functions are proposed to compare matched pairs under a unified framework, where various types of outcomes for measuring treatment response (including continuous, ordinal, and discrete outcomes) can easily be accommodated. We establish the Fisher consistency and convergence rate of M-learning. Through extensive simulation studies, we show that M-learning outperforms existing methods when propensity scores are misspecified or when unmeasured confounders are present in certain scenarios. Lastly, we apply M-learning to estimate optimal personalized second-line treatments for type 2 diabetes patients to achieve better glycemic control or reduce major complications using EHRs from New York Presbyterian Hospital. 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.7461749.v2（source: https://tandf.figshare.com/articles/dataset/Matched_Learning_for_Optimizing_Individualized_Treatment_Strategies_Using_Electronic_Health_Records/7461749）。