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

Acute Coronary Syndrome III: Diagnostic Studies01:30

Acute Coronary Syndrome III: Diagnostic Studies

218
Diagnosing acute coronary syndrome or ACS begins with a thorough patient history. Notable symptoms include central, crushing chest pain radiating to the left arm, neck, jaw, or back, along with shortness of breath, sweating (diaphoresis), nausea, vomiting, dizziness, and palpitations.It is crucial to note any history of cardiac illnesses and assess risk factors, including age, gender, smoking, hypertension, diabetes, hyperlipidemia, and a sedentary lifestyle.During physical examination, vital...
218
Angina III: Clinical Manifestations and Assessment01:29

Angina III: Clinical Manifestations and Assessment

203
Angina manifests as chest pain, tightness, or squeezing discomfort typically located behind the breastbone. It can radiate to the neck, jaw, shoulders, and inner aspects of the upper arms, most commonly the left arm. Patients may experience shortness of breath, fatigue, profuse sweating, dizziness, indigestion, heartburn, palpitations, anxiety, and vomiting as accompanying symptoms. This pain often lasts a few minutes and is triggered by physical exertion, emotional stress, heavy meals, or cold...
203

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

Geotechnical challenges of urban expansion in Mila Town (NE Algeria): an integrated Engineering Ground Model (EGM) approach.

Scientific reports·2026
Same author

Metaheuristic-optimized interaction-aware deep learning with large language model assistance for data-driven water quality prediction.

Scientific reports·2026
Same author

Human-inspired hyperparameter optimization for long-horizon forecasting of freshwater and desalination per-capita dynamics.

Scientific reports·2026
Same author

An optimization-driven hierarchical deep learning approach using the Gray Langurs algorithm for data-driven seismic activity prediction.

Scientific reports·2026
Same author

Accurate surgery time prediction (ASTP) strategy based on artificial intelligence techniques.

Scientific reports·2026
Same author

Deposit characterizations and engineering-geotechical modeling for sustainable urbanisation in the Mila basin (NE Algeria).

Scientific reports·2026

相关实验视频

Updated: Jan 17, 2026

Semi-automated Optical Heartbeat Analysis of Small Hearts
12:10

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

12.8K

一种使用人工智能技术的新组合心脏病发作诊断 (EHAD) 模型.

Bahaa El-Din Waleed1, El-Sayed M El-Kenawy2,3, Sherif Ibrahim4

  • 1Department of Applied Health Sciences, Higher Technological Institute of Applied Health Sciences, Mansoura, Egypt. bahaaafia@std.mans.edu.e.g.

Scientific reports
|September 15, 2025
PubMed
概括

一种用于诊断心脏病发作 (心肌梗塞) 的新组合模型显示,与现有方法相比,其准确性有所提高. 这种混合方法结合了多个机器学习分类器,以更快,更精确地检测心脏病发作.

关键词:
人工智能的人工智能是人工智能.诊断系统的诊断系统组合模型模型组合模型功能选择 功能选择心脏病发作 心脏病发作

更多相关视频

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.3K
In Silico Clinical Trials for Cardiovascular Disease
09:09

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

2.2K

相关实验视频

Last Updated: Jan 17, 2026

Semi-automated Optical Heartbeat Analysis of Small Hearts
12:10

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

12.8K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.3K
In Silico Clinical Trials for Cardiovascular Disease
09:09

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

2.2K

科学领域:

  • 心脏病学 心脏病学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 心肌梗塞 (心脏病发作) 是全球主要的死亡原因.
  • 当前的机器学习和人工智能诊断方法缺乏最佳准确性.
  • 改善诊断准确度对于更好的患者结果至关重要.

研究的目的:

  • 为心脏病发作检测引入一种新的混合诊断模型.
  • 通过组合技术提高心脏病发作诊断的准确性和速度.

主要方法:

  • 开发了集体心脏病发作诊断 (EHAD) 模型.
  • 使用集成分类技术 (ECT) 集成支持矢量机器 (SVM),长短期内存 (LSTM) 和人工神经网络 (ANN).
  • 用员工多数投票 (MV) 进行最终决策.

主要成果:

  • 与现有模型相比,EHAD模型表现出优越的性能.
  • 在回忆,精度,F1得分和准确度方面,EHAD取得了很高的成绩.
  • 统计分析证实了该模型的有效性.

结论:

  • 拟议的EHAD模型为心脏病发作诊断提供了更准确,更有效的方法.
  • 这种混合组合方法代表了人工智能驱动的心血管诊断的重大进步.
  • 进一步的开发可以通过精确和及时的诊断来改善患者的结果.