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

Computed Tomography01:10

Computed Tomography

7.6K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
7.6K
Dense Connective Tissue01:13

Dense Connective Tissue

10.9K
Dense connective tissue contains more collagen fibers than loose connective tissue. As a consequence, it displays greater resistance to stretching. There are two major categories of dense connective tissue— regular and irregular.
Dense Regular Connective Tissue
In dense regular connective tissue, fibers are arranged parallel to each other, enhancing its tensile strength and resistance to stretching in the direction of the fiber orientations. Ligaments and tendons are made of dense regular...
10.9K
Three-Dimensional Force System01:30

Three-Dimensional Force System

3.2K
In mechanical engineering, a three-dimensional force system is a system of forces acting in three dimensions, with forces applied along the x, y, and z coordinate axes. The three-dimensional force system is an important concept in mechanical engineering, as it allows engineers to understand and analyze the behavior of objects and structures in three dimensions. By understanding the forces acting on a system, engineers can design more efficient and effective mechanical systems that can withstand...
3.2K
Shear Diagram01:27

Shear Diagram

1.9K
In the study of beam mechanics, shear diagrams play a crucial role in understanding the distribution of shear forces along the length of a beam. Consider a beam AB that is supported at both ends and subjected to perpendicular loads.
First, a free-body diagram of the beam is drawn, representing all the external forces and internal reactions acting on the beam. One can calculate the reaction forces at each support by employing the equilibrium equations of force and moment. The vertical component...
1.9K
Elastic Curve from the Load Distribution01:16

Elastic Curve from the Load Distribution

588
The structural behavior of beams under distributed loads is critical for engineering analysis, which focuses on predicting how beams bend and react under such conditions. Different types of beams (e.g., cantilever, supported, or overhanging) behave differently under distributed load conditions.
For all beams, the analysis of the beam's reaction to distributed loads begins by understanding the relationship between a beam's load and the resulting shear forces and bending moments. Initially, this...
588
Energy Line and Hydraulic Gradient Line01:27

Energy Line and Hydraulic Gradient Line

2.9K
Based on Bernoulli's equation, the energy line (EL) and hydraulic grade line (HGL) provide graphical representations of energy distribution in a fluid flow system. For steady, incompressible, inviscid flows, Bernoulli's equation is expressed as:
2.9K

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

Analyzing and Mitigating Model Drift in Acute Kidney Injury Prediction for Hospitalized Patients.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same author

SurfFold: a unified model for protein inverse folding by integrating surface and structural information.

Bioinformatics (Oxford, England)·2025
Same author

SMART: a new patient similarity estimation framework for enhanced predictive modeling in acute kidney injury.

Journal of the American Medical Informatics Association : JAMIA·2025
Same author

Autoencoder-Based Representation Learning for Similar Patients Retrieval From Electronic Health Records: Comparative Study.

JMIR medical informatics·2025
Same author

Clustering analysis of multi-site electronic health records reveals distinct subphenotypes in stage-1 acute kidney injury.

Communications medicine·2025
Same author

A Comparative Analysis of Patient Similarity Measures for Outcome Prediction.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2025
Same journal

Sensitivity Analyses of a Scoring System for a Contraception Decision Aid.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same journal

Improving electronic health record processing of large language models via retrieval-augmented generation: A case study on dietary supplements.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same journal

Developing a User-Centered Mobile Application Prototype: Bridging Lower-Limb Fracture Care from Skilled Nursing Facility and Back to the Community.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same journal

KERAP: A Knowledge-Enhanced Reasoning Approach for Accurate Zero-shot Diagnosis Prediction Using Multi-agent LLMs.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same journal

Automating Adjudication of Cardiovascular Events Using Large Language Models.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same journal

Predictive Factors and State-Level Barriers to Postpartum Birth Control Usage in the United States: Insights from PRAMS Phase 8.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
查看所有相关文章

相关实验视频

Updated: May 3, 2026

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
12:09

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

13.2K

DeepJ:用于患者轨迹建模的可差聚合的图形卷积变压器.

Deyi Li1, Zijun Yao2, Muxuan Liang3

  • 1Department of Health outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.

AMIA ... Annual Symposium proceedings. AMIA Symposium
|February 23, 2026
PubMed
概括
此摘要是机器生成的。

深度患者旅程 (DeepJ) 模型跨患者遭遇的医疗事件相互作用,改善患者结果预测. 这种图形学习方法捕捉了时间依赖性,以便更好地分层风险.

更多相关视频

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery
09:41

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Published on: May 20, 2016

11.3K
3D Printing Model of a Patient's Specific Lumbar Vertebra
07:30

3D Printing Model of a Patient's Specific Lumbar Vertebra

Published on: April 14, 2023

2.7K

相关实验视频

Last Updated: May 3, 2026

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
12:09

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

13.2K
A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery
09:41

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Published on: May 20, 2016

11.3K
3D Printing Model of a Patient's Specific Lumbar Vertebra
07:30

3D Printing Model of a Patient's Specific Lumbar Vertebra

Published on: April 14, 2023

2.7K

科学领域:

  • 医疗信息学医学信息学
  • 医疗保健中的人工智能
  • 电子健康记录的图形学习

背景情况:

  • 电子健康记录 (EHR) 数据包含复杂的医疗事件相互作用.
  • 现有的图形学习方法与纵向数据扎,无法建模交叉遇到的时间依赖.
  • 静态图方法限制了对患者随时间的旅程的分析.

研究的目的:

  • 为了介绍Deep Patient Journey (DeepJ),一个新的图形卷积变压器模型.
  • 为了有效地捕捉内部遭遇和相互遭遇的医疗事件相互作用.
  • 为了确定时间和功能相关的医疗事件的群体,以预测患者的结果.

主要方法:

  • 开发了DeepJ,一个图形卷积变压器模型.
  • 集成的可微分图集群用于增强的交互建模.
  • 应用DeepJ对结构化EHR数据进行纵向分析.

主要成果:

  • DeepJ成功地捕获了医疗事件的互动.
  • 确定了与患者结果相关的关键事件集群.
  • 在预测任务中表现优于五个最先进的基线模型.

结论:

  • DeepJ 提供了使用 EHR 数据改进的患者旅程建模.
  • 该模型增强了可解释性,并证明了患者风险分层的潜力.
  • DeepJ推进了图形学习在临床信息学中的应用.