脓毒症风险评估：认知风险管理机器人在临床外科单元实施后的回顾性分析

数据集获取（Figshare）

• 源站标题：Sepsis risk assessment: a retrospective analysis after a cognitive risk management robot (Robot Laura®) implementation in a clinical-surgical unit
• DOI：10.6084/m9.figshare.7678319.v1
• 发布日期：2019-02-06
• 文件数：5 个
• 文件总体积：62.06 KB
• 源站页面：https://scielo.figshare.com/articles/dataset/Sepsis_risk_assessment_a_retrospective_analysis_after_a_cognitive_risk_management_robot_Robot_Laura_implementation_in_a_clinical-surgical_unit/7678319

命令行下载

curl -L -o "2446-4740-reng-2446-4740180021-gf01.jpg" "https://ndownloader.figshare.com/files/14273171"
curl -L -o "Table_1.xls" "https://ndownloader.figshare.com/files/14273180"
curl -L -o "Table_2.xls" "https://ndownloader.figshare.com/files/14273189"
curl -L -o "Table_3.xls" "https://ndownloader.figshare.com/files/14273195"
curl -L -o "Table_4.xls" "https://ndownloader.figshare.com/files/14273201"

文件清单

• 2446-4740-reng-2446-4740180021-gf01.jpg（39.53 KB）
• Table_1.xls（5.63 KB）
• Table_2.xls（5.63 KB）
• Table_3.xls（5.63 KB）
• Table_4.xls（5.63 KB）

文件总体积 62.06 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

Abstract Introduction This study aimed at evaluating the impact of the implementation of a cognitive robot (Robot Laura™) on processes related to the identification and care of patients with risk of sepsis in a clinical-surgical unit of a private hospital in Curitiba-PR. Methods The study data were obtained from the retrospective review of medical records of patients identified with infection and/or sepsis, in the period of six months before and after the implementation of such technology in the hospital. In addition, the Average Attendance Time (AAT) was obtained from the autonomous reading of the robot. Results The average time/median until antibiotic prescription from the first identified sign of infection, with or without sepsis, was 390/77 and 109/58 minutes, respectively, in the six months before and after implementation of the technology. However, this difference was not statistically significant (p = 0.85). Regarding AAT, it was possible to observe a reduction from 305 to 280 minutes when comparing the periods of six months before and after the implementation of the technology (p = 0.02). Conclusion Technologies such as this may be promising in helping healthcare professionals to identify risky situations for patients, as well as in assisting them to optimize the care required. However, further studies, with a greater number of subjects and with different scenarios, are necessary to consistently validate the results found.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.7678319.v1（source: https://scielo.figshare.com/articles/dataset/Sepsis_risk_assessment_a_retrospective_analysis_after_a_cognitive_risk_management_robot_Robot_Laura_implementation_in_a_clinical-surgical_unit/7678319）。