人工智能在预测急症护理环境中院内心脏骤停和呼吸骤停中的应用——对临床实践的意义（表格1）

数据集获取（Figshare）

• 源站标题：Table 1_Use of artificial intelligence in predicting in-hospital cardiac and respiratory arrest in an acute care environment—implications for clinical practice.docx
• DOI：10.3389/fmedt.2025.1681059.s001
• 发布日期：2025-10-10
• 文件数：1 个
• 文件总体积：84.58 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Use_of_artificial_intelligence_in_predicting_in-hospital_cardiac_and_respiratory_arrest_in_an_acute_care_environment_implications_for_clinical_practice_docx/30326989

命令行下载

curl -L -o "Table 1_Use of artificial intelligence in predicting in-hospital cardiac and respiratory arrest in an acute care environment—implications for clinical.docx" "https://ndownloader.figshare.com/files/58616197"

文件清单

• Table 1_Use of artificial intelligence in predicting in-hospital cardiac and respiratory arrest in an acute care environment—implications for clinical.docx（84.58 KB）

文件总体积 84.58 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

BackgroundArtificial intelligence (AI)-based models can augment clinical decision-making, including prediction, diagnosis, and treatment, in all aspects of medicine.
Research questionsThe current systematic review aims to provide a summary of existing data about the role of machine learning (ML) techniques in predicting in-hospital cardiac arrest, life-threatening ventricular arrhythmias, and respiratory arrest.
MethodsThe study was conducted in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) framework. PubMed, Embase, and Web of Science without any restriction were searched to extract relevant manuscripts until October 20, 2023. Additionally, the reference list of all potential studies was searched to identify further relevant articles. Original publications were regarded as eligible if they only recruited adult patients (≥18 years of age), employed AI/ML algorithms for predicting cardiac arrest, life-threatening ventricular arrhythmias, and respiratory arrest in the setting of critical care, used data gathered from wards with critically ill patients (ICUs, cardiac ICUs, and emergency departments), and were published in English. The following information was extracted: first author, journal, ward, sample size, performance and features of ML and conventional models, and outcomes.
ResultsML algorithms have been used for cardiac arrest prediction using easily obtained variables as inputs. ML algorithms showed promising results (AUC 0.73–0.96) in predicting cardiac arrest in different settings, including critically ill ICU patients, patients in the emergency department and patients with sepsis, they demonstrated variable performance (AUC 0.54–0.94) in predicting respiratory arrest in COVID-19 patients, as well as other clinical settings.
ConclusionML algorithms have shown promising results in predicting in-hospital cardiac and respiratory arrest using readily available clinical data. These algorithms may enhance early identification of high risk patients and support timely interventions, thereby reducing mortality and morbidity rates. However, the prospective validation of these algorithms and their integration into c

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmedt.2025.1681059.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Use_of_artificial_intelligence_in_predicting_in-hospital_cardiac_and_respiratory_arrest_in_an_acute_care_environment_implications_for_clinical_practice_docx/30326989）。