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

Health Information Technology and Healthcare Information System01:30

Health Information Technology and Healthcare Information System

805
Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
805
Integrated Healthcare System01:20

Integrated Healthcare System

1.5K
An integrated healthcare system (IHS) is a set of organizations that provides for or arranges to provide coordinated and continuous service to a defined population. The IHS takes responsibility for that particular population's health status and outcome, both clinically and fiscally. An integrated healthcare system is a well-organized, well-coordinated, and collaborative network. The integrated delivery system is a network that connects different healthcare providers to deliver organized,...
1.5K
Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

5.6K
The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
5.6K
Methods Of Healthcare Delivery System01:26

Methods Of Healthcare Delivery System

3.2K
At the different levels of the healthcare system, we see varying methods of healthcare used. These methods include managed care systems, case management, and primary healthcare.
Managed Care System:
The managed care system is designed to control the cost while maintaining the quality of care. The patient's care from admission to discharge is planned by the primary care provider or the case manager, also known as the gatekeeper. In a managed care system, the number of care providers is...
3.2K
Secondary Healthcare System01:11

Secondary Healthcare System

1.4K
Secondary healthcare is offered by a specialist, generally in hospitals or clinics for patients referred by primary healthcare providers. It occurs when a person has an illness or injury that requires specific medical care. Secondary care is often referred to as acute care. Secondary care can range from uncomplicated care to repair a minor laceration or treat a strep throat infection to more complicated emergent care, such as treating a head injury sustained in an automobile accident. Whatever...
1.4K
Healthcare Agencies II01:17

Healthcare Agencies II

689
There are various healthcare agencies in the United States—some of which are managed by religious institutions and others by different government branches.
Parish nursing is a growing specialty nursing profession that focuses on holistic healthcare, health promotion, and illness prevention. It blends professional nursing practice with a health ministry, focusing on health and healing within the context of a Christian community. Parish nurses serve as health educators, referral sources,...
689

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

Non-IID and aware federated intrusion detection with PBFT with secured model aggregation for multi institutional healthcare internet of things networks.

Scientific reports·2026
Same author

Enhanced detection of network intrusions and anomalies in internet of things applications using a hybrid artificial intelligence model combining CNN and LSTM.

Scientific reports·2026
Same author

Hybrid feature selection for IoMT based intrusion detection system for integrating mutual information filtering with deep learning based accelerated metaheuristic optimization.

Scientific reports·2026
Same author

Advanced channel estimation in OTFS and NOMA using deep bayesian gaussian processes and compressive sensing.

Scientific reports·2026
Same author

A hybrid blockchain based deep learning model for multivector attack detection in internet of things enabled healthcare systems.

Scientific reports·2026
Same author

Applying the defense model to strengthen information security with artificial intelligence in computer networks of the financial services sector.

Scientific reports·2025
Same journal

Therapeutic potential of crude protein extracts from two Egyptian freshwater snails Lanistes carinatus and Bellamya unicolor.

Scientific reports·2026
Same journal

Microbial contamination of donor corneas and post-keratoplasty endophthalmitis: a comparison between Japanese and U.S. eye banks using cold storage.

Scientific reports·2026
Same journal

Prevalence and contributing factors of virological non-suppression among adult patients on first-line antiretroviral therapy in tertiary hospitals in Ethiopia.

Scientific reports·2026
Same journal

An in vitro comparison of color stability between alkasite and different restorative materials in various staining solutions.

Scientific reports·2026
Same journal

Toward accessible mRNA LNP formulation: systematic evaluation of mixing strategies and key parameters.

Scientific reports·2026
Same journal

A network analysis of personality traits, mentalizing, and psychological health in Chinese college students.

Scientific reports·2026
查看所有相关文章

相关实验视频

Updated: Jun 8, 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

基于边缘计算的组合学习模型用于医疗保健决策系统.

Asir Chandra Shinoo Robert Vincent1, Sudhakar Sengan2

  • 1Department of Computer Science and Engineering, PSN College of Engineering and Technology, Tirunelveli, Tamil Nadu, 627451, India. racshinoo2022@gmail.com.

Scientific reports
|November 6, 2024
PubMed
概括
此摘要是机器生成的。

这项研究介绍了临床决策支持系统 (CDSS) 的集成极端学习机器 (EN-ELM),以改善慢性疾病的诊断. 该EN-ELM显著提高预测准确度,帮助医疗保健专业人员改善患者的治疗结果.

关键词:
准确度 准确度 准确度 准确度 准确度适应性的合成材料临床决策支持系统边缘计算是一种边缘计算.极端学习的机器学习.机器学习 机器学习

更多相关视频

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

963
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K

相关实验视频

Last Updated: Jun 8, 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
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

963
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K

科学领域:

  • 医疗信息学 医疗信息学
  • 机器学习 机器学习
  • 医疗保健中的人工智能

背景情况:

  • 慢性疾病的流行率不断上升,需要准确和快速的诊断和治疗程序.
  • 传统的机器学习 (ML) 方法通常在对复杂疾病的可靠预测方面表现出局限性.
  • 临床决策支持系统 (CDSS) 旨在改善患者状况并帮助医疗保健专业人员的决策.

研究的目的:

  • 提出一个先进的临床决策支持系统 (CDSS),使用集成极端学习机器 (EN-ELM) 算法.
  • 提高慢性疾病的诊断和医疗治疗程序的可靠性和准确性.
  • 解决传统ML中的挑战,如过度拟合,阶级不平衡和异常值.

主要方法:

  • 开发一个集成极端学习机器 (EN-ELM) 算法,将多个训练有素的预测器结合起来,以减轻过度拟合.
  • 整合数据处理技术,如自适应合成 (ADASYN) 和隔离森林 (iForest),以处理异常值和阶级不平衡.
  • 与边缘计算 (EC) 模型的兼容性,用于实时计算和减少系统集成需求.

主要成果:

  • 拟议的CDSS框架在各种医疗数据集的分类性能方面取得了显著的改进.
  • 该EN-ELM模型实现了高准确率:肝细胞癌 (HCC) 的99.36%,宫癌的98.15%,慢性脏病的97.85%,心脏病的97.06%,心律不整的96.72%.
  • 85%的最佳ELM分类值被认为是提高预测模型准确性的最有效方法.

结论:

  • 开发的CDSS,由EN-ELM与ADASYN和iForest一起提供动力,为准确的慢性疾病诊断提供了强大的解决方案.
  • 该系统的高精度和实时功能可以显著改善患者护理和临床决策.
  • 跨多种医疗数据集的进一步验证证实了CDSS在彻底改变慢性疾病管理方面的潜力.