预测老年人跌倒及干预时机：多水平逻辑回归模型与成本分析

数据集获取（Figshare）

• 源站标题：Predicting Falls and When to Intervene in Older People: A Multilevel Logistical Regression Model and Cost Analysis
• DOI：10.1371/journal.pone.0159365
• 发布日期：2016-07-22
• 文件数：7 个
• 文件总体积：208.15 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Predicting_Falls_and_When_to_Intervene_in_Older_People_A_Multilevel_Logistical_Regression_Model_and_Cost_Analysis/3499718

命令行下载

curl -L -o "S1 File.DOCX" "https://ndownloader.figshare.com/files/5530679"
curl -L -o "S1 Table.DOCX" "https://ndownloader.figshare.com/files/5530685"
curl -L -o "S2 Table.DOCX" "https://ndownloader.figshare.com/files/5530694"
curl -L -o "S3 Table.DOCX" "https://ndownloader.figshare.com/files/5530709"
curl -L -o "S4 Table.DOCX" "https://ndownloader.figshare.com/files/5530727"
curl -L -o "S5 Table.DOCX" "https://ndownloader.figshare.com/files/5530769"
curl -L -o "S6 Table.DOCX" "https://ndownloader.figshare.com/files/5530739"

文件清单

• S1 File.DOCX（16.20 KB）
• S1 Table.DOCX（45.90 KB）
• S2 Table.DOCX（34.84 KB）
• S3 Table.DOCX（23.56 KB）
• S4 Table.DOCX（19.30 KB）
• S5 Table.DOCX（45.59 KB）
• S6 Table.DOCX（22.77 KB）

文件总体积 208.15 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

Introduction
Falls are the leading cause of injury in older people. Reducing falls could reduce financial pressures on health services. We carried out this research to develop a falls risk model, using routine primary care and hospital data to identify those at risk of falls, and apply a cost analysis to enable commissioners of health services to identify those in whom savings can be made through referral to a falls prevention service.
Methods
Multilevel logistical regression was performed on routinely collected general practice and hospital data from 74751 over 65’s, to produce a risk model for falls. Validation measures were carried out. A cost-analysis was performed to identify at which level of risk it would be cost-effective to refer patients to a falls prevention service. 95% confidence intervals were calculated using a Monte Carlo Model (MCM), allowing us to adjust for uncertainty in the estimates of these variables.
Results
A risk model for falls was produced with an area under the curve of the receiver operating characteristics curve of 0.87. The risk cut-off with the highest combination of sensitivity and specificity was at p = 0.07 (sensitivity of 81% and specificity of 78%). The risk cut-off at which savings outweigh costs was p = 0.27 and the risk cut-off with the maximum savings was p = 0.53, which would result in referral of 1.8% and 0.45% of the over 65’s population respectively. Above a risk cut-off of p = 0.27, costs do not exceed savings.
Conclusions
This model is the best performing falls predictive tool developed to date; it has been developed on a large UK city population; can be readily run from routine data; and can be implemented in a way that optimises the use of health service resources. Commissioners of health services should use this model to flag and refer patients at risk to their falls service and save resources.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0159365（source: https://plos.figshare.com/articles/dataset/Predicting_Falls_and_When_to_Intervene_in_Older_People_A_Multilevel_Logistical_Regression_Model_and_Cost_Analysis/3499718）。