中国心血管疾病医疗成本：基于机器学习的横断面研究（表1）

数据集获取（Figshare）

• 源站标题：Table_1_Health care costs of cardiovascular disease in China: a machine learning-based cross-sectional study.docx
• DOI：10.3389/fpubh.2023.1301276.s001
• 发布日期：2023-11-06
• 文件数：1 个
• 文件总体积：622.81 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Health_care_costs_of_cardiovascular_disease_in_China_a_machine_learning-based_cross-sectional_study_docx/24502912

命令行下载

curl -L -o "Table_1_Health care costs of cardiovascular disease in China: a machine learning-based cross-sectional study.docx" "https://ndownloader.figshare.com/files/43046890"

文件清单

• Table_1_Health care costs of cardiovascular disease in China: a machine learning-based cross-sectional study.docx（622.81 KB）

文件总体积 622.81 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

BackgroundCardiovascular disease (CVD) causes substantial financial burden to patients with the condition, their households, and the healthcare system in China. Health care costs for treating patients with CVD vary significantly, but little is known about the factors associated with the cost variation. This study aims to identify and rank key determinants of health care costs in patients with CVD in China and to assess their effects on health care costs.
MethodsData were from a survey of patients with CVD from 14 large tertiary grade-A general hospitals in S City, China, between 2018 and 2020. The survey included information on demographic characteristics, health conditions and comorbidities, medical service utilization, and health care costs. We used re-centered influence function regression to examine health care cost concentration, decomposing and estimating the effects of relevant factors on the distribution of costs. We also applied quantile regression forests—a machine learning approach—to identify the key factors for predicting the 10th (low), 50th (median), and 90th (high) quantiles of health care costs associated with CVD treatment.
ResultsOur sample included 28,213 patients with CVD. The 10th, 50th and 90th quantiles of health care cost for patients with CVD were 6,103 CNY, 18,105 CNY, and 98,637 CNY, respectively. Patients with high health care costs were more likely to be older, male, and have a longer length of hospital stay, more comorbidities, more complex medical procedures, and emergency admissions. Higher health care costs were also associated with specific CVD types such as cardiomyopathy, heart failure, and stroke.
ConclusionMachine learning methods are useful tools to identify determinants of health care costs for patients with CVD in China. Findings may help improve policymaking to alleviate the financial burden of CVD, particularly among patients with high health care costs.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fpubh.2023.1301276.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Health_care_costs_of_cardiovascular_disease_in_China_a_machine_learning-based_cross-sectional_study_docx/24502912）。