交互式多目标优化寻找膝骨关节炎中最受偏好的运动疗法模式

数据集获取（Figshare）

• 源站标题：Interactive multiobjective optimization for finding the most preferred exercise therapy modality in knee osteoarthritis
• DOI：10.6084/m9.figshare.18319338.v1
• 发布日期：2022-01-13
• 文件数：2 个
• 文件总体积：4.14 MB
• 源站页面：https://tandf.figshare.com/articles/dataset/Interactive_multiobjective_optimization_for_finding_the_most_preferred_exercise_therapy_modality_in_knee_osteoarthritis/18319338

命令行下载

curl -L -o "Supplementary_material_A_B_and_C_knee_OA_22.6.2020_.pdf" "https://ndownloader.figshare.com/files/33080493"
curl -L -o "Supplementary_material_D_cost_assessment.pdf" "https://ndownloader.figshare.com/files/33080496"

文件清单

• Supplementary_material_A_B_and_C_knee_OA_22.6.2020_.pdf（3.55 MB）
• Supplementary_material_D_cost_assessment.pdf（597.08 KB）

文件总体积 4.14 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

There are no explicit guidelines or tools available to support clinicians in selecting exercise therapy modalities according to the characteristics of individual patients despite the apparent need.
This study develops a methodology based on a novel multiobjective optimization model and examines its feasibility as a decision support tool to support healthcare professionals in comparing different modalities and identifying the most preferred one based on a patient’s needs.
Thirty-one exercise therapy modalities were considered from 21 randomized controlled trials. A novel interactive multiobjective optimization model was designed to characterize the efficacy of an exercise therapy modality based on five objectives: minimizing cost, maximizing pain reduction, maximizing disability improvement, minimizing the number of supervised sessions, and minimizing the length of the treatment period. An interactive model incorporates clinicians’ preferences in finding the most preferred exercise therapy modality for each need. Multiobjective optimization methods are mathematical algorithms designed to identify the optimal balance between multiple conflicting objectives among available solutions/alternatives. They explicitly evaluate the conflicting objectives and support decision-makers in identifying the best balance. An experienced research-oriented physiotherapist was involved as a decision-maker in the interactive solution process testing the proposed decision support tool.
The proposed methodology design and interactive process of the tool, including preference information, graphs, and exercise suggestions following the preferences, can help clinicians to find the most preferred exercise therapy modality based on a patient’s needs and health status; paving the way to individualize recommendations.
We examined the feasibility of our decision support tool using an interactive multiobjective optimization method designed to help clinicians balance between conflicting objectives to find the most preferred exercise therapy modality for patients with knee osteoarthritis. The proposed methodology is generic enough to be applied in any field of medical and healthcare settings,

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.18319338.v1（source: https://tandf.figshare.com/articles/dataset/Interactive_multiobjective_optimization_for_finding_the_most_preferred_exercise_therapy_modality_in_knee_osteoarthritis/18319338）。