感染性坏死性胰腺炎4周内或超过4周手术机器学习分类数据表
感染性坏死性胰腺炎手术时机（4周内/超过4周）机器学习分类研究的PDF数据表，含223例患者临床因素分析。
基本信息
资源简介
感染性坏死性胰腺炎4周内或超过4周手术机器学习分类数据表
【一句话概览】
坏死性胰腺炎手术时机仍存争议，随机对照试验未解决。本研究分析四川大学华西医院223例感染性胰腺坏死手术患者，采用逻辑回归、支持向量机和随机森林，结合或不结合生成对抗网络模拟，将手术干预时机分为4周内或超过4周。结果显示，白介素6、感染性坏死、发热发生和C反应蛋白是决定手术时机的重要因素。机器学习模型可有效预测手术干预时机，并识别与感染性坏死性胰腺炎手术时机及术后生存相关的关键因素（来源 figshare，CC BY 4.0 许可）。
【数据集背景】
本数据集源站标题为《Data_Sheet_1_Classification of Infected Necrotizing Pancreatitis for Surgery Within or Beyond 4 Weeks Using Machine Learning.pdf》。配套论文《Diagnostic strategy and timing of intervention in infected necrotizing pancreatitis: an international expert survey and case vignette study》发表于 HPB（2015），DOI：10.1111/hpb.12491。源站首次发布：2020-06-04。
【数据内容】
源站原始描述：Background: The timing of surgery for necrotizing pancreatitis remains a matter of controversial debate, which has not been resolved by randomized controlled trial (RCT). This study aims to classify surgical timing within or beyond 4 weeks for patients with infected necrotizing pancreatitis by using machine learning methods. Methods: This study analyzed 223 patients who underwent surgery for infected pancreatic necrosis at West China Hospital of Sichuan University. We used logistic regression, s…
【数据结构与技术规格】
文件数 1 个，合计 382.11 KB。Data_Sheet_1_Classification of Infected Necrotizing Pancreatitis for Surgery Within or Beyond 4 Weeks Using Machine Learning.pdf：application/pdf，382.11 KB。数据集 DOI：10.3389/fbioe.2020.00541.s001。
【主题与分类】
源站学科分类：Bioprocessing, Bioproduction and Bioproducts、Industrial Biotechnology Diagnostics (incl. Biosensors)、Industrial Microbiology (incl. Biofeedstocks)、Industrial Molecular Engineering of Nucleic Acids and Proteins、Industrial Biotechnology not elsewhere classified、Medical Biotechnology Diagnostics (incl. Biosensors)。主题标签：classification、surgery、timing、machine learning、necrotizing pancreatitis。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Classification_of_Infected_Necrotizing_Pancreatitis_for_Surgery_Within_or_Beyond_4_Weeks_Using_Machine_Learning_pdf/12422912
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Lan, Lan; Guo, Qiang; Zhang, Zhigang; Zhao, Weiling; Yang, Xiaoyan; Lu, Huimin; et al. (2020). Data_Sheet_1_Classification of Infected Necrotizing Pancreatitis for Surgery Within or Beyond 4 Weeks Using Machine Learning.pdf. Frontiers. Dataset
【使用注意】
· 源站最后修改于 2023-06-06，引用时请注意版本时效。
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取（Figshare）
- 源站标题：Data_Sheet_1_Classification of Infected Necrotizing Pancreatitis for Surgery Within or Beyond 4 Weeks Using Machine Learning.pdf
- DOI：10.3389/fbioe.2020.00541.s001
- 发布日期：2020-06-04
- 文件数：1 个
- 文件总体积：382.11 KB
- 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Classification_of_Infected_Necrotizing_Pancreatitis_for_Surgery_Within_or_Beyond_4_Weeks_Using_Machine_Learning_pdf/12422912
命令行下载
curl -L -o "Data_Sheet_1_Classification of Infected Necrotizing Pancreatitis for Surgery Within or Beyond 4 Weeks Using Machine Learning.pdf" "https://ndownloader.figshare.com/files/22893314"
文件清单
Data_Sheet_1_Classification of Infected Necrotizing Pancreatitis for Surgery Within or Beyond 4 Weeks Using Machine Learning.pdf（382.11 KB）
文件总体积 382.11 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
Background: The timing of surgery for necrotizing pancreatitis remains a matter of controversial debate, which has not been resolved by randomized controlled trial (RCT). This study aims to classify surgical timing within or beyond 4 weeks for patients with infected necrotizing pancreatitis by using machine learning methods.
Methods: This study analyzed 223 patients who underwent surgery for infected pancreatic necrosis at West China Hospital of Sichuan University. We used logistic regression, support vector machine, and random forest with/without the simulation of generative adversarial networks to classify the surgical intervention within or beyond 4 weeks in the patients with infected necrotizing pancreatitis.
Results: Our analyses showed that interleukin 6, infected necrosis, the onset of fever and C-reactive protein were important factors in determining the timing of surgical intervention (Conclusions: Machine learning models can be used to predict timing of surgical intervention effectively and key factors associated with surgical timing and postoperative survival are identified for infected necrotizing pancreatitis.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.3389/fbioe.2020.00541.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Classification_of_Infected_Necrotizing_Pancreatitis_for_Surgery_Within_or_Beyond_4_Weeks_Using_Machine_Learning_pdf/12422912）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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