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 VII: EMR01:30

Methods of Documentation VII: EMR

1.4K
Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare...
1.4K
Purpose of Health Records II01:19

Purpose of Health Records II

1.4K
Health records serve various essential purposes in the healthcare system. Here are some key purposes:
1.4K
Time-Series Graph00:54

Time-Series Graph

5.0K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
5.0K
Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

6.2K
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...
6.2K
Generation Time01:22

Generation Time

1.4K
Bacterial generation time, the period required for a bacterial population to double during its exponential growth phase, serves as a critical measure of microbial growth dynamics under optimal conditions. This parameter varies significantly across bacterial species and can be influenced by factors such as temperature, pH, and the availability of nutrients. For example, Escherichia coli can achieve a generation time of approximately 20 minutes, while Mycobacterium tuberculosis exhibits a much...
1.4K

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

The construct validity of real-world digital mobility outcomes in people with COPD.

ERJ open research·2026
Same author

GeneTEK: Low-power and high-performance FPGA scalable architecture for exact unit-cost edit distance.

Computers in biology and medicine·2026
Same author

Construct validity of real-world digital mobility outcomes in patients after proximal femoral fracture: a cross-sectional observational study.

Scientific reports·2026
Same author

Acoustic Emission Biomarkers for the Detection and Monitoring of Early Knee Osteoarthritis: Protocol for a Prospective, Single-Center, Exploratory Study.

JMIR research protocols·2026
Same author

Moving beyond the hospital: in-depth characterization of daily-life mobility in patients with atypical Parkinsonian disorders.

NPJ Parkinson's disease·2026
Same author

Benchmark of EEG-based seizure detection algorithms with SzCORE<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

相关实验视频

Updated: Jan 18, 2026

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data
04:13

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Published on: November 13, 2019

12.8K

时间EHR:基于图像的时间序列生成用于电子健康记录.

Hojjat Karami, Mary-Anne Hartley, David Atienza

    IEEE journal of biomedical and health informatics
    |June 6, 2025
    PubMed
    概括
    此摘要是机器生成的。

    我们开发了TimEHR,这是一个用于电子健康记录 (EHR) 时间序列数据的新型生成对抗网络 (GAN). 该模型有效生成现实的电子健康记录数据,解决缺失值和不规则抽样等挑战.

    更多相关视频

    Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
    10:17

    Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

    Published on: April 11, 2025

    1.6K
    Time-Lapse Imaging of Neuronal Arborization using Sparse Adeno-Associated Virus Labeling of Genetically Targeted Retinal Cell Populations
    13:13

    Time-Lapse Imaging of Neuronal Arborization using Sparse Adeno-Associated Virus Labeling of Genetically Targeted Retinal Cell Populations

    Published on: March 19, 2021

    3.3K

    相关实验视频

    Last Updated: Jan 18, 2026

    Computer-based Multitaper Spectrogram Program for Electroencephalographic Data
    04:13

    Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

    Published on: November 13, 2019

    12.8K
    Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
    10:17

    Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

    Published on: April 11, 2025

    1.6K
    Time-Lapse Imaging of Neuronal Arborization using Sparse Adeno-Associated Virus Labeling of Genetically Targeted Retinal Cell Populations
    13:13

    Time-Lapse Imaging of Neuronal Arborization using Sparse Adeno-Associated Virus Labeling of Genetically Targeted Retinal Cell Populations

    Published on: March 19, 2021

    3.3K

    科学领域:

    • 人工智能的人工智能
    • 生物医学信息学 生物医学信息学
    • 机器学习 机器学习

    背景情况:

    • 电子健康记录 (EHR) 包含复杂的时间序列数据.
    • 电子健康记录时间序列的挑战包括不规则的抽样,缺失的值和高维度.
    • 现有的生成模型难以准确地表示EHR数据.

    研究的目的:

    • 提出一个新的生成对抗网络 (GAN) 模型,命名为TimEHR.
    • 为了应对从EHRs生成时间序列数据的独特挑战.
    • 为了提高生成的EHR数据的保真性,实用性和隐私性.

    主要方法:

    • 时间EHR采用了一种新的方法,将时间序列数据视为图像.
    • 该模型使用2D卷积内核来表示数据.
    • 它基于两个有条件的GAN:一个用于缺失模式,另一个用于时间序列值.

    主要成果:

    • 与最先进的方法相比,TimEHR表现出卓越的性能.
    • 在三个真实世界EHR数据集上进行了评估.
    • 该模型在忠实性,实用性和隐私指标方面表现出色.

    结论:

    • 时间EHR是一个有效的GAN模型,用于生成合成EHR时间序列数据.
    • 提出的方法成功地处理了失踪模式和不规则的抽样.
    • 时间EHR为医疗保健中的数据增强和隐私保护提供了一个有前途的解决方案AI.