院外心脏骤停贝叶斯模型验证结果
基于日本OHCA登记数据的贝叶斯模型验证表格数据集,含15个临床变量和预后结局,用于预测良好神经学预后。

基本信息
资源简介
院外心脏骤停贝叶斯模型验证结果
【数据集背景】
本数据集源站标题为《The Bayesian model validation result.》。源站首次发布:2023-09-28。
【数据内容】
源站原始描述:Out-of-hospital cardiac arrest (OHCA) is linked to a poor prognosis and remains a public health concern. Several studies have predicted good neurological outcomes of OHCA. In this study, we used the Bayesian network to identify variables closely associated with good neurological survival outcomes in patients with OHCA. This was a retrospective observational study using the Japan Association for Acute Medicine OHCA registry. Fifteen explanatory variables were used, and the outcome was one-month s…
【数据结构与技术规格】
文件数 1 个,合计 5.63 KB。数据集 DOI:10.1371/journal.pone.0291258.t003。
【主题与分类】
源站学科分类:Genetics、Neuroscience、Biotechnology、Environmental Sciences not elsewhere classified、Biological Sciences not elsewhere classified、Mathematical Sciences not elsewhere classified。主题标签:public health concern、motor response component、hospital cardiac arrest、good neurological outcomes、glasgow coma scale、glasgow &# 8211、59 &# 8211、2014 &# 8211、1 &# 8211、fifteen explanatory variables。
【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_Bayesian_model_validation_result_/24214358
使用许可:CC BY 4.0(https://creativecommons.org/licenses/by/4.0/)。
规范引用:Shinada, Kota; Matsuoka, Ayaka; Koami, Hiroyuki; Sakamoto, Yuichiro (2023). The Bayesian model validation result.. PLOS ONE. Dataset
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 Bayesian model validation result.
- DOI:10.1371/journal.pone.0291258.t003
- 发布日期:2023-09-28
- 文件数:1 个
- 文件总体积:5.63 KB
- 源站页面:https://plos.figshare.com/articles/dataset/The_Bayesian_model_validation_result_/24214358
命令行下载
curl -L -o "Table 3.xls" "https://ndownloader.figshare.com/files/42499784"
文件清单
Table 3.xls(5.63 KB)
文件总体积 5.63 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
Out-of-hospital cardiac arrest (OHCA) is linked to a poor prognosis and remains a public health concern. Several studies have predicted good neurological outcomes of OHCA. In this study, we used the Bayesian network to identify variables closely associated with good neurological survival outcomes in patients with OHCA. This was a retrospective observational study using the Japan Association for Acute Medicine OHCA registry. Fifteen explanatory variables were used, and the outcome was one-month survival with Glasgow–Pittsburgh cerebral performance category (CPC) 1–2. The 2014–2018 dataset was used as training data. The variables selected were identified and a sensitivity analysis was performed. The 2019 dataset was used for the validation analysis. Four variables were identified, including the motor response component of the Glasgow Coma Scale (GCS M), initial rhythm, age, and absence of epinephrine. Estimated probabilities were increased in the following order: GCS M score: 2–6; epinephrine: non-administered; initial rhythm: spontaneous rhythm and shockable; and age: <58 and 59–70 years. The validation showed a sensitivity of 75.4% and a specificity of 95.4%. We identified GCS M score of 2–6, initial rhythm (spontaneous rhythm and shockable), younger age, and absence of epinephrine as variables associated with one-month survival with CPC 1–2. These variables may help clinicians in the decision-making process while treating patients with OHCA.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0291258.t003(source: https://plos.figshare.com/articles/dataset/The_Bayesian_model_validation_result_/24214358)。
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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