Table1_使用机器学习识别有发生常见严重不良事件组风险的小细胞肺癌患者

数据集获取（Figshare）

• 源站标题：Table1_Identification of small cell lung cancer patients who are at risk of developing common serious adverse event groups with machine learning.DOCX
• DOI：10.3389/fdsfr.2023.1267623.s001
• 发布日期：2024-11-25
• 文件数：1 个
• 文件总体积：847.75 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table1_Identification_of_small_cell_lung_cancer_patients_who_are_at_risk_of_developing_common_serious_adverse_event_groups_with_machine_learning_DOCX/27901593

命令行下载

curl -L -o "Table1_Identification of small cell lung cancer patients who are at risk of developing common serious adverse event groups with machine learning.DOCX" "https://ndownloader.figshare.com/files/50785434"

文件清单

• Table1_Identification of small cell lung cancer patients who are at risk of developing common serious adverse event groups with machine learning.DOCX（847.75 KB）

文件总体积 847.75 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: Across multiple studies, the most common serious adverse event groups that Small Cell Lung Cancer (SCLC) patients experience, whilst undergoing chemotherapy treatment, are: Blood and Lymphatic Disorders, Infections and Infestations together with Metabolism and Nutrition Disorders. The majority of the research that investigates the relationship between adverse events and SCLC patients, focuses on specific adverse events such as neutropenia and thrombocytopenia.
Aim: This study aims to utilise machine learning in order to identify those patients who are at risk of developing common serious adverse event groups, as well as their specific adverse event classification grade.
Methods: Data from five clinical trial studies were analysed and 12 analysis groups were formed based on the serious adverse event group and grade.
Results: The best test runs for each of the models were able to produce an area under the curve (AUC) score of at least 0.714. The best model was the Blood and Lymphatic Disorder group, SAE grade 0 vs. grade 3 (best AUC = 1, sensitivity rate = 0.84, specificity rate = 0.96).
Conclusion: The top features that contributed to this prediction were total bilirubin, alkaline phosphatase, and age. Future work should investigate the relationship between these features and common SAE groups.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fdsfr.2023.1267623.s001（source: https://frontiersin.figshare.com/articles/dataset/Table1_Identification_of_small_cell_lung_cancer_patients_who_are_at_risk_of_developing_common_serious_adverse_event_groups_with_machine_learning_DOCX/27901593）。