数据表2：“少言多图”：与临床医生创建和分享来自远程健康监测系统的数据可视化以改善癌症疼痛管理

数据集获取（Figshare）

• 源站标题：Data Sheet 2_“Less words, more pictures”: creating and sharing data visualizations from a remote health monitoring system with clinicians to improve cancer pain management.docx
• DOI：10.3389/fdgth.2025.1520990.s002
• 发布日期：2025-04-23
• 文件数：1 个
• 文件总体积：75.57 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_2_Less_words_more_pictures_creating_and_sharing_data_visualizations_from_a_remote_health_monitoring_system_with_clinicians_to_improve_cancer_pain_management_docx/28845092

命令行下载

curl -L -o "Data Sheet 2_“Less words, more pictures”: creating and sharing data visualizations from a remote health monitoring system with clinicians to improve c.docx" "https://ndownloader.figshare.com/files/53910716"

文件清单

• Data Sheet 2_“Less words, more pictures”: creating and sharing data visualizations from a remote health monitoring system with clinicians to improve c.docx（75.57 KB）

文件总体积 75.57 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

BackgroundThe Behavioral and Environmental Sensing and Intervention for Cancer (BESI-C) is a remote health monitoring system (RHMS) developed by our interdisciplinary team that collects holistic physiological, behavioral, psychosocial, and contextual data related to pain from dyads of patients with cancer and their family caregivers via environmental and wearable (smartwatch) sensors.
MethodsR, Python, and Canva software were used to create a series of static and interactive data visualizations (e.g., visual representations of data in the form of graphs, figures, or pictures) from de-identified BESI-C data to share with palliative care clinicians during virtual and in-person 1-hour feedback sessions. Participants were shown a sequence of 5–6 different data visualizations related to patient and caregiver self-reported pain events, environmental factors, and quality of life indicators, completed an electronic survey that assessed clarity, usefulness, and comprehension, and then engaged in a structured discussion. Quantitative survey results were descriptively analyzed and “think aloud” qualitative comments thematically summarized and used to iterate data visualizations between feedback sessions.
ResultsSix to 12 interdisciplinary palliative care clinicians from an academic medical center, a local hospice, and a community hospital within Central Virginia participated in five data visualization feedback sessions. Both survey results and group discussion feedback revealed a preference for more familiar, simpler data visualizations that focused on the physical aspects of pain assessment, such as number of high intensity pain events and response to pharmacological interventions. Preferences for degree of data granularity and content varied by discipline and care delivery model, and there was mixed interest in seeing caregiver reported data. Overall, non-physician participants expressed greater interest in visualizations that included environmental variables impacting pain and non-pharmacological interventions.
ConclusionClinicians desired higher-level (i.e., less granular/detailed) views of complex sensing data with a “take home” message that can be quickly processed.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fdgth.2025.1520990.s002（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_2_Less_words_more_pictures_creating_and_sharing_data_visualizations_from_a_remote_health_monitoring_system_with_clinicians_to_improve_cancer_pain_management_docx/28845092）。