基于机器学习模型预测成人创伤性脑损伤住院死亡率的系统回顾与荟萃分析数据集（表1）
包含机器学习模型预测成人创伤性脑损伤住院死亡率系统回顾的表1数据，汇总14项研究的样本、模型和AUC信息。
基本信息
资源简介
基于机器学习模型预测成人创伤性脑损伤住院死亡率的系统回顾与荟萃分析数据集（表1）
【一句话概览】
本数据集是 figshare 公开的结构化表格数据集，收录 1 个文件、合计 38.67 KB，主题涵盖 traumatic brain injury、machine learning、mortality predictor、meta-analysis、inpatient mortality，可用于统计建模、特征工程与队列分析，CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《Table_1_Machine learning-based model for predicting inpatient mortality in adults with traumatic brain injury: a systematic review and meta-analysis.DOCX》。配套论文《A Machine Learning–Based Prognostic Model for the Prediction of Early Death After Traumatic Brain Injury: Comparison with the Corticosteroid Randomization After Significant Head Injury (CRASH) Model》发表于 World Neurosurgery（2022），DOI：10.1016/j.wneu.2022.06.130。源站首次发布：2023-12-14。
【数据内容】
源站原始描述：Background and objectivePredicting mortality from traumatic brain injury facilitates early data-driven treatment decisions. Machine learning has predicted mortality from traumatic brain injury in a growing number of studies, and the aim of this study was to conduct a meta-analysis of machine learning models in predicting mortality from traumatic brain injury. MethodsThis systematic review and meta-analysis included searches of PubMed, Web of Science and Embase from inception to June 2023, supple…
中文译文（机器翻译，仅供快速理解，请以原文为准）：背景与目的 预测创伤性脑损伤后的死亡率有助于早期基于数据的治疗决策。越来越多的研究利用机器学习预测创伤性脑损伤后的死亡率，本研究旨在对机器学习模型预测创伤性脑损伤死亡率进行荟萃分析。 方法 本系统综述和荟萃分析检索了PubMed、Web of Science和Embase自建库至2023年6月的文献，并手动检索了研究参考文献和综述文章作为补充。使用Stata 16.0软件进行数据分析。本研究已在PROSPERO注册（CRD2023440875）。 结果 共纳入14项研究。这些研究在总体样本、模型类型和模型验证方面存在显著差异。预测模型表现良好，合并AUC为0.90（95% CI：0.87至0.92）。 结论 总体而言，本研究强调了机器学习模型在判定创伤性脑损伤后死亡率方面具有出色的预测能力。然而，需要注意的是，最佳机器学习建模方法尚未确定。 系统综述注册 https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=440875，标识符CRD2023440875。
【数据结构与技术规格】
文件数 1 个，合计 38.67 KB。Table_1_Machine learning-based model for predicting inpatient mortality in adults with traumatic brain injury: a systematic review and meta-analysis.DOCX：application/vnd.openxmlformats-officedocument.wordprocessingml.document，38.67 KB。数据集 DOI：10.3389/fnins.2023.1285904.s002。
【主题与分类】
源站学科分类：Radiology and Organ Imaging、Decision Making、Autonomic Nervous System、Cellular Nervous System、Biological Engineering、Central Nervous System。主题标签：traumatic brain injury、machine learning、mortality predictor、meta-analysis、inpatient mortality。
【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/Table_1_Machine_learning-based_model_for_predicting_inpatient_mortality_in_adults_with_traumatic_brain_injury_a_systematic_review_and_meta-analysis_DOCX/24803964
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Wu, Zhe; Lai, Jinqing; Huang, Qiaomei; Lin, Long; Lin, Shu; Chen, Xiangrong; et al. (2023). Table_1_Machine learning-based model for predicting inpatient mortality in adults with traumatic brain injury: a systematic review and meta-analysis.DOCX. Frontiers. 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：Table_1_Machine learning-based model for predicting inpatient mortality in adults with traumatic brain injury: a systematic review and meta-analysis.DOCX
- DOI：10.3389/fnins.2023.1285904.s002
- 发布日期：2023-12-14
- 文件数：1 个
- 文件总体积：38.67 KB
- 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Machine_learning-based_model_for_predicting_inpatient_mortality_in_adults_with_traumatic_brain_injury_a_systematic_review_and_meta-analysis_DOCX/24803964
命令行下载
curl -L -o "Table_1_Machine learning-based model for predicting inpatient mortality in adults with traumatic brain injury: a systematic review and meta-analysis.DOCX" "https://ndownloader.figshare.com/files/43629942"
文件清单
Table_1_Machine learning-based model for predicting inpatient mortality in adults with traumatic brain injury: a systematic review and meta-analysis.DOCX（38.67 KB）
文件总体积 38.67 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 and objectivePredicting mortality from traumatic brain injury facilitates early data-driven treatment decisions. Machine learning has predicted mortality from traumatic brain injury in a growing number of studies, and the aim of this study was to conduct a meta-analysis of machine learning models in predicting mortality from traumatic brain injury.
MethodsThis systematic review and meta-analysis included searches of PubMed, Web of Science and Embase from inception to June 2023, supplemented by manual searches of study references and review articles. Data were analyzed using Stata 16.0 software. This study is registered with PROSPERO (CRD2023440875).
ResultsA total of 14 studies were included. The studies showed significant differences in the overall sample, model type and model validation. Predictive models performed well with a pooled AUC of 0.90 (95% CI: 0.87 to 0.92).
ConclusionOverall, this study highlights the excellent predictive capabilities of machine learning models in determining mortality following traumatic brain injury. However, it is important to note that the optimal machine learning modeling approach has not yet been identified.
Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=440875, identifier CRD2023440875.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.3389/fnins.2023.1285904.s002（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Machine_learning-based_model_for_predicting_inpatient_mortality_in_adults_with_traumatic_brain_injury_a_systematic_review_and_meta-analysis_DOCX/24803964）。
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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