研究人群及出院早期结局影响因素的逐步逻辑回归分析
包含颅内出血患者入院生理和实验室指标及出院结局，通过逐步逻辑回归分析识别影响早期预后的独立危险因素。
基本信息
资源简介
研究人群及出院早期结局影响因素的逐步逻辑回归分析
【数据集背景】
本数据集源站标题为《The stepwise logistic regression analysis of the study population and the potential factors affecting early outcome at discharge.》。配套论文《Platelet Count and Early Outcome in Patients with Spontaneous Cerebellar Hemorrhage: A Retrospective Study》发表于 PLOS ONE（2015），DOI：10.1371/journal.pone.0119109。源站首次发布：2015-03-17。
【数据内容】
源站原始描述：Note: 1. SBP = systolic blood pressure; DBP = diastolic blood pressure; HR = heart rate; BT = body temperature; ICH = intracranial hemorrhage; BUN = blood urea nitrogen; AST = aspartate aminotransferase. 2. t-test was used to compare the difference between two groups and significant variables was evaluated by binary logistic regression with forward stepwise. The independent risk factors in the final model were presented as odds ratios, including 95% confidence intervals (CI). ***P≦0.001; **P≦0.0…
【数据结构与技术规格】
文件数 1 个，合计 9.73 KB。Table_3.xls：application/vnd.ms-excel，9.73 KB。数据集 DOI：10.1371/journal.pone.0119109.t003。
【主题与分类】
源站学科分类：Biological Sciences。主题标签：GOS score 4、outcome group、outcome.ConclusionsWe firstly show、Spontaneous Cerebellar Hemorrhage、pc、ch、Glasgow Outcome Scale、Retrospective Study IntroductionThe importance。
【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://plos.figshare.com/articles/dataset/_The_stepwise_logistic_regression_analysis_of_the_study_population_and_the_potential_factors_affecting_early_outcome_at_discharge_/1339499
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Lin, Ching-Yueh; Chang, Chih-Ya; Sun, Chia-Hung; Li, Tsung-Ying; Chen, Liang-Cheng; Chang, Shin-Tsu; et al. (2015). The stepwise logistic regression analysis of the study population and the potential factors affecting early outcome at discharge.. PLOS ONE. Dataset
【使用注意】
· 源站最后修改于 2023-06-09，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：The stepwise logistic regression analysis of the study population and the potential factors affecting early outcome at discharge.
- DOI：10.1371/journal.pone.0119109.t003
- 发布日期：2015-03-17
- 文件数：1 个
- 文件总体积：9.73 KB
- 源站页面：https://plos.figshare.com/articles/dataset/_The_stepwise_logistic_regression_analysis_of_the_study_population_and_the_potential_factors_affecting_early_outcome_at_discharge_/1339499
命令行下载
curl -L -o "Table_3.xls" "https://ndownloader.figshare.com/files/1953660"
文件清单
Table_3.xls（9.73 KB）
文件总体积 9.73 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
Note:
- SBP = systolic blood pressure; DBP = diastolic blood pressure; HR = heart rate; BT = body temperature; ICH = intracranial hemorrhage; BUN = blood urea nitrogen; AST = aspartate aminotransferase.
- t-test was used to compare the difference between two groups and significant variables was evaluated by binary logistic regression with forward stepwise. The independent risk factors in the final model were presented as odds ratios, including 95% confidence intervals (CI).
***P≦0.001;
**P≦0.01;
- p≦0.05.
The stepwise logistic regression analysis of the study population and the potential factors affecting early outcome at discharge.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0119109.t003（source: https://plos.figshare.com/articles/dataset/_The_stepwise_logistic_regression_analysis_of_the_study_population_and_the_potential_factors_affecting_early_outcome_at_discharge_/1339499）。
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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