Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Pre-Procedural Guidelines for Assessing Blood Pressure01:10

Pre-Procedural Guidelines for Assessing Blood Pressure

526
Accurate blood pressure assessment is crucial for diagnosing and managing various health conditions. To ensure the reliability of these measurements, healthcare professionals must adhere to standardized pre-procedural guidelines. These guidelines enhance patient safety and improve the overall quality of healthcare. The following steps are essential for obtaining accurate and consistent blood pressure readings, from using the appropriate tools to ensuring effective communication with the...
526

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

GreenAid: a confidence-weighted ensemble deep learning system for real-time plant disease detection and management.

Scientific reports·2026
Same author

Improving airport security with IoT-powered deep learning methods for threat detection and intelligent recommendation systems.

Scientific reports·2026
Same author

Few-shot android malware classification with quantum-enhanced prototypical learning and drift detection.

Scientific reports·2026
Same author

AI-driven fault detection and classification in photovoltaic systems using deep learning techniques.

Scientific reports·2026
Same author

FibroidX: Vision Transformer-Powered Prognosis and Recurrence Prediction for Uterine Fibroids Using Ultrasound Images.

Cancers·2026
Same author

Explainable Computational Imaging for Precision Oncology: An Interpretable Deep Learning Framework for Bladder Cancer Histopathology Diagnosis.

Bioengineering (Basel, Switzerland)·2026

相关实验视频

Updated: Jun 14, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.0K

CardioRiskNet:一种基于人工智能的混合模型,用于对心血管疾病的可解释风险预测和预后.

Fatma M Talaat1,2, Ahmed R Elnaggar3, Warda M Shaban4

  • 1Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33516, Egypt.

Bioengineering (Basel, Switzerland)
|August 29, 2024
PubMed
概括

人工智能模型 CardioRiskNet 准确地使用主动学习和注意力机制预测心血管疾病 (CVD) 风险. 这种先进的工具超越了传统方法,提供了更好的患者护理和疾病管理.

关键词:
积极学习是积极学习.心血管疾病 (CVD) 是一种心血管疾病.无法解释的人工智能风险预测风险预测

更多相关视频

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
07:51

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Published on: September 26, 2018

7.6K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K

相关实验视频

Last Updated: Jun 14, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.0K
Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
07:51

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Published on: September 26, 2018

7.6K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K

科学领域:

  • 人工智能在医学中的应用
  • 心血管疾病研究研究
  • 机器学习用于医疗保健

背景情况:

  • 心血管疾病 (CVD) 是全球主要的死亡原因,需要改进风险评估.
  • 传统的CVD风险评估方法在精度和适应性方面存在局限性.
  • 需要先进的策略来克服传统风险预测模型的缺陷.

研究的目的:

  • 引入 CardioRiskNet,一种基于人工智能的混合模型,用于增强心血管疾病风险评估和预后.
  • 解决传统CVD风险预测方法的局限性.
  • 为医疗保健专业人员开发一个透明和准确的AI工具.

主要方法:

  • 卡迪奥风险网集成了数据预处理,特征选择,可解释AI (XAI),主动学习和注意力机制.
  • 该模型采用主动学习来进行代样本选择和注意力机制来实现动态特征聚焦.
  • 通过XAI集成,在风险预测过程中确保了可解释性和透明度.

主要成果:

  • CardioRiskNet实现了卓越的性能,准确率为98.7%,灵敏度为98.7%,特异性为99%,F1-Score为98.7%.
  • 实验结果表明该模型能够准确评估和预测心血管疾病风险.
  • 人工智能模型显著优于传统的风险评估方法.

结论:

  • 卡迪奥风险网为心血管疾病风险管理提供了一种新且高性能的方法.
  • 该研究强调了积极学习和人工智能的潜力,以促进心血管疾病预后.
  • CardioRiskNet为医疗保健专业人员提供了一个强大的工具,改善了患者护理和疾病管理.