脑出血护理中人工智能应用：对临床和护理实践的启示——叙述性文献综述 Data Sheet 1

数据集获取（Figshare）

• 源站标题：Data Sheet 1_Artificial intelligence applications in intracerebral hemorrhage care: implications for clinical and nursing practice - a narrative literature review.docx
• DOI：10.3389/fresc.2025.1620335.s001
• 发布日期：2025-07-07
• 文件数：1 个
• 文件总体积：22.26 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Artificial_intelligence_applications_in_intracerebral_hemorrhage_care_implications_for_clinical_and_nursing_practice_-_a_narrative_literature_review_docx/29487395

命令行下载

curl -L -o "Data Sheet 1_Artificial intelligence applications in intracerebral hemorrhage care: implications for clinical and nursing practice - a narrative liter.docx" "https://ndownloader.figshare.com/files/56018924"

文件清单

• Data Sheet 1_Artificial intelligence applications in intracerebral hemorrhage care: implications for clinical and nursing practice - a narrative liter.docx（22.26 KB）

文件总体积 22.26 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

Little is known about how artificial intelligence tools are utilized across the different stages of intracerebral hemorrhage care or how they contribute to clinical decision-making and patient outcomes in this population. This narrative review aimed to explore current applications of artificial intelligence in the clinical management of patients with intracerebral hemorrhage. A comprehensive search was conducted across five electronic databases (PubMed, CINAHL Plus with Full Text, Ovid MEDLINE, ProQuest, and Web of Science), supplemented by additional manual searches. This review included studies published in English between January 1, 2014, and December 31, 2024. Seven studies examining the application of artificial intelligence in the acute and post-acute phases of intracerebral hemorrhage care were included. In the acute phase, machine learning models such as Random Forest and XGBoost outperform traditional prognostic scoring systems, offering clinicians more precise tools for early risk stratification. In the post-acute phase, AI contributes to continuity of care by supporting data completion, rehabilitation planning, and remote rehabilitation, thereby enhancing patient-centered nursing practice with high predictive accuracy and practical utility. These findings suggest that artificial intelligence holds significant promise for enhancing prognosis prediction, clinical decision-making, and continuity of care in patients with intracerebral hemorrhage.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fresc.2025.1620335.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Artificial_intelligence_applications_in_intracerebral_hemorrhage_care_implications_for_clinical_and_nursing_practice_-_a_narrative_literature_review_docx/29487395）。