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

Purpose of Health Records I01:11

Purpose of Health Records I

1.8K
The vital purpose of health records is to provide a complete and accurate account of a patient's medical history, including communication, diagnostic and therapeutic orders, care planning, research, and quality review.
Here's a breakdown of how health records serve these purposes:
1.8K
Purpose of Health Records II01:19

Purpose of Health Records II

1.5K
Health records serve various essential purposes in the healthcare system. Here are some key purposes:
1.5K
Electron Orbital Model01:18

Electron Orbital Model

73.1K
Orbitals are the areas outside of the atomic nucleus where electrons are most likely to reside. They are characterized by different energy levels, shapes, and three-dimensional orientations. The location of electrons is described most generally by a shell or principal energy level, then by a subshell within each shell, and finally, by individual orbitals found within the subshells.
The first shell is closest to the nucleus, and it has only one subshell with a single spherical orbital called the...
73.1K
State Space Representation01:27

State Space Representation

610
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
610
Graphical Representation of Inequalities01:28

Graphical Representation of Inequalities

233
The graph of the equation where y equals x squared forms a curve known as a parabola. This curve acts as a boundary in the coordinate plane, dividing it into distinct regions based on the relative position of points.When the equality sign in the equation is replaced with an inequality—such as greater than, less than, greater than or equal to, or less than or equal to—the graphical representation changes from a single curve into a broader shaded area that signifies the set of all...
233
Control Volume and System Representations01:16

Control Volume and System Representations

1.6K
Two key frameworks are employed to analyze mass, energy, and momentum transfer: the control volume approach and the system approach. These frameworks offer different perspectives, depending on whether the focus is on a specific region in space (control volume approach) or a defined mass of fluid (system approach).
The control volume approach considers a stationary region in space through which fluid flows. This region is bounded by a control surface.  For instance, in the case of water...
1.6K

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

Predicting progression of Alzheimer's disease using blood-based multi-omics data.

Bioinformatics advances·2026
Same author

From General-Purpose to Disease-Specific Features: Aligning LLM Embeddings on a Disease-Specific Biomedical Knowledge Graph for Drug Repurposing.

bioRxiv : the preprint server for biology·2026
Same author

MultiGEOmics: Graph-Based Integration of Multi-Omics via Biological Information Flows.

bioRxiv : the preprint server for biology·2026
Same author

scAURA: Alignment- and Uniformity-based Graph Debiased Contrastive Representation Architecture for Self-Supervised Clustering of Single-Cell Transcriptomics.

bioRxiv : the preprint server for biology·2026
Same author

A Systematic Fairness Evaluation of Racial Bias in Alzheimer's Disease Diagnosis Using Machine Learning Models.

bioRxiv : the preprint server for biology·2025
Same author

ProtFun: a protein function prediction model using graph attention networks with a protein large language model.

Bioinformatics advances·2025
Same journal

Layered social competition coordinates reproductive hierarchy formation in ants.

bioRxiv : the preprint server for biology·2026
Same journal

Combination epigenetic-targeted therapy increases the immunogenicity of poorly immunogenic sarcomas.

bioRxiv : the preprint server for biology·2026
Same journal

Loss of LanC-like proteins delays post-injury regeneration of aging skeletal muscles.

bioRxiv : the preprint server for biology·2026
Same journal

Integrative Transfer Network: Deep Transfer Learning Across Populations and Prediction Targets.

bioRxiv : the preprint server for biology·2026
Same journal

Confidence-supported label-free metabolic imaging with FPhaS phase autofluorescence microscopy.

bioRxiv : the preprint server for biology·2026
Same journal

Sequence-encoded autoinhibition couples mRNA decapping activity to phase separation.

bioRxiv : the preprint server for biology·2026
查看所有相关文章

相关实验视频

Updated: Feb 14, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.6K

DyGraphTrans:一种时间图表表示学习框架,用于从电子健康记录中建模疾病进展.

Most Tahmina Rahman, Mohammad Al Olaimat, Serdar Bozdag

    bioRxiv : the preprint server for biology
    |February 13, 2026
    PubMed
    概括
    此摘要是机器生成的。

    DyGraphTrans提供了一个新的框架,用于使用电子健康记录 (EHR) 预测早期疾病. 这种动态图表方法有效地处理患者数据,提高临床见解的准确性和可解释性.

    更多相关视频

    TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients
    09:00

    TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients

    Published on: April 13, 2021

    5.4K
    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

    8.2K

    相关实验视频

    Last Updated: Feb 14, 2026

    Constructing and Visualizing Models using Mime-based Machine-learning Framework
    06:19

    Constructing and Visualizing Models using Mime-based Machine-learning Framework

    Published on: July 22, 2025

    2.6K
    TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients
    09:00

    TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients

    Published on: April 13, 2021

    5.4K
    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

    8.2K

    科学领域:

    • 计算生物学是一种计算生物学.
    • 医疗信息学医学信息学
    • 机器学习是机器学习.

    背景情况:

    • 电子健康记录 (EHR) 为疾病预测提供了丰富的纵向患者数据.
    • 现有的EHR分析计算方法经常受到高内存使用量,计算成本和缺乏可解释性的困扰.
    • 在保持准确性和可解释性的同时,高效地处理大规模的EHR数据是一个重大挑战.

    研究的目的:

    • 引入DyGraphTrans,这是一个动态图表表示学习框架,用于患者电子病历数据.
    • 在内存消耗,计算成本和可解释性方面解决现有方法的局限性.
    • 通过电子健康记录 (EHR) 实现准确和可解释的早期疾病预测.

    主要方法:

    • 代表患者的电子病历数据作为时间图的序列.
    • 利用患者的节点,时间临床属性的节点特征和患者相似性的边缘.
    • 采用滑动窗口机制,以减少内存消耗,同时保持时间上下文.
    • 共同捕捉患者的相似性和时间演变,以有效的记忆和可解释的方式.

    主要成果:

    • 在阿尔茨海默病神经成像计划 (ADNI),国家阿尔茨海默病协调中心 (NACC) 和重症监护医疗信息中心 (MIMIC-IV) 数据集上,DyGraphTrans表现出强大的预测性能.
    • 该模型实现了准确的早期死亡率预测和疾病进展预测.
    • 解释性分析显示与已知的临床风险因素保持一致.

    结论:

    • DyGraphTrans提供了一个高效和可解释的解决方案,用于利用EHR数据进行疾病预测.
    • 该框架成功地模拟了患者数据的局部时间依赖性和长期全球趋势.
    • DyGraphTrans为推进临床信息学和精准医学中的计算方法提供了一个有前途的方法.