优化和个性化真性红细胞增多症患者放血计划表1

数据集获取（Figshare）

• 源站标题：Table_1_Optimized and Personalized Phlebotomy Schedules for Patients Suffering From Polycythemia Vera.XLSX
• DOI：10.3389/fphys.2020.00328.s002
• 发布日期：2020-04-17
• 文件数：1 个
• 文件总体积：12.11 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Optimized_and_Personalized_Phlebotomy_Schedules_for_Patients_Suffering_From_Polycythemia_Vera_XLSX/12142881

命令行下载

curl -L -o "Table_1_Optimized and Personalized Phlebotomy Schedules for Patients Suffering From Polycythemia Vera.XLSX" "https://ndownloader.figshare.com/files/22330218"

文件清单

• Table_1_Optimized and Personalized Phlebotomy Schedules for Patients Suffering From Polycythemia Vera.XLSX（12.11 KB）

文件总体积 12.11 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

Polycythemia vera (PV) is a slow-growing type of blood cancer, where the production of red blood cells (RBCs) increase considerably. The principal treatment for targeting the symptoms of PV is bloodletting (phlebotomy) at regular intervals based on data derived from blood counts and physician assessments based on experience. Model-based decision support can help to identify optimal and individualized phlebotomy schedules to improve the treatment success and reduce the number of phlebotomies and thus negative side effects of the therapy. We present an extension of a simple compartment model of the production of RBCs in adults to capture patients suffering from PV. We analyze the model’'s properties to show the plausibility of its assumptions. We complement this with numerical results using exemplary PV patient data. The model is then used to simulate the dynamics of the disease and to compute optimal treatment plans. We discuss heuristics and solution approaches for different settings, which include constraints arising in real-world applications, where the scheduling of phlebotomies depends on appointments between patients and treating physicians. We expect that this research can support personalized clinical decisions in cases of PV.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fphys.2020.00328.s002（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Optimized_and_Personalized_Phlebotomy_Schedules_for_Patients_Suffering_From_Polycythemia_Vera_XLSX/12142881）。