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

Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
For example, a patient with a chronic illness...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...

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

Standardizing Corneal Transplantation Records Using openEHR: Case Study.

JMIR medical informatics·2024
Same author

Exploring trends and autonomy levels of adaptive business intelligence in healthcare: A systematic review.

PloS one·2024
Same author

Towards effective clinical decision support systems: A systematic review.

PloS one·2022
查看所有相关文章

相关实验视频

Updated: Jul 3, 2026

Emergency Undocking in Robotic Surgery: A Simulation Curriculum
06:48

Emergency Undocking in Robotic Surgery: A Simulation Curriculum

Published on: May 20, 2018

9.2K

提高医疗保健机构的手术安排,使用元启发式优化模型:算法验证研究

João Lopes1, Tiago Guimarães1, Júlio Duarte1

  • 1ALGORITMI Research Centre, University of Minho, Rua da Universidade, Braga, 4800-058, Portugal, 351 934373667.

JMIR medical informatics
|February 12, 2025
PubMed
概括
此摘要是机器生成的。

人工智能 (AI) 启发式模型优化了手术安排,大大减少了处罚和等待时间. 这种人工智能驱动的方法提高了医院的效率和以患者为中心的护理.

关键词:
人工智能的人工智能是人工智能.医疗保健 医疗保健 医疗保健这种模型是元启发式模型.模型优化 模型优化外科手术安排时间表手术安排问题 手术安排问题

更多相关视频

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
09:10

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

1.7K
Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions
06:18

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

Published on: April 5, 2024

936

相关实验视频

Last Updated: Jul 3, 2026

Emergency Undocking in Robotic Surgery: A Simulation Curriculum
06:48

Emergency Undocking in Robotic Surgery: A Simulation Curriculum

Published on: May 20, 2018

9.2K
Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
09:10

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

1.7K
Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions
06:18

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

Published on: April 5, 2024

936

科学领域:

  • 医疗信息学 医疗信息学
  • 运营研究 运营研究
  • 人工智能的人工智能

背景情况:

  • 医疗保健系统在优化临床和组织流程方面面临挑战.
  • COVID-19 疫情凸显了以患者为中心的护理和高效的决策的需要.
  • 手术安排是复杂的,容易做出低于最佳的决定,影响等待列表和成本.

研究的目的:

  • 为优化外科手术中心管理提出一种启发式方法.
  • 与葡萄牙一家领先的医院 (CHUdSA) 合作,实施和测试这种方法.

主要方法:

  • 分析CHUdSA的外科手术安排过程.
  • 基于人工智能 (AI) 的启发式模型的应用,特别是登 (HC) 和模拟回火 (SA) 算法.
  • 评估模型在最大限度地减少调度处罚方面的有效性.

主要成果:

  • 人工智能启发式模型在调度效率方面显示出显著的改进.
  • 登 (HC) 算法安排了96.7%的手术,在特定的专业领域没有任何处罚.
  • 与手动方法相比,模拟回火 (SA) 算法也提高了调度率,并减少了处罚.

结论:

  • 将AI解决方案集成到外科手术安排中可以提高效率并降低成本.
  • 人工智能驱动的策略可以尽量减少患者等待时间,并最大限度地利用资源.
  • 这些人工智能算法在适应医疗环境和提高外科手术结果方面具有变革性影响.