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

Nursing Interventions II: Selecting and Classifying the Nursing Interventions01:29

Nursing Interventions II: Selecting and Classifying the Nursing Interventions

2.2K
Creating and executing a nursing diagnosis helps nurses plan care and guide patient, family, and community interventions. They are developed based on a patient's physical evaluation and support measuring the outcomes. It is not recommended to select random interventions throughout the planning process. Instead, consider the following six essential factors when choosing interventions:
2.2K
Time-Series Graph00:54

Time-Series Graph

4.4K
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...
4.4K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

321
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
321
Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.3K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

106
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
106
Nursing Interventions I: Taxonomy of Nursing Interventions01:03

Nursing Interventions I: Taxonomy of Nursing Interventions

2.6K
Nursing interventions are chosen as part of the planning process to achieve patient outcomes. Once nursing diagnoses are determined, the goals and outcomes are specified, then the nursing interventions are selected and individualized according to the patient's situation.
A nursing intervention is a treatment or action based on scientific concepts and knowledge from the nursing, behavioral, and physical sciences. Identifying and prioritizing nursing interventions based on the desired outcome...
2.6K

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

Digintel metasurface of extensive reprogrammable morphing.

Science advances·2026
Same author

Engineering an injectable and tunable hydrogel as a potential vitreous substitute.

Journal of materials chemistry. B·2026
Same author

Reproductive tract and follicular fluid microbiota and probiotic interventions in IVF/ICSI outcomes: a systematic review.

BMC pregnancy and childbirth·2026
Same author

A flexible arched artificial photoreceptor constructed by photodeformable liquid crystal polymers and its application in vision restoration.

Smart molecules : open access·2026
Same author

Multi-Omics Integration Reveals Synergistic Metabolic Rewiring Underpinning Growth Acceleration in a Hybrid Pompano "Chenhai No. 1".

Animals : an open access journal from MDPI·2026
Same author

Discovery and Engineering of a Rat Endogenous Retrovirus Reverse Transcriptase for Efficient Prime Editing.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

相关实验视频

Updated: Jun 30, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.6K

预测ICU干预:基于多变量时间序列图形卷积神经网络的透明决策支持模型

Zhen Xu, Jinjin Guo, Lang Qin

    IEEE journal of biomedical and health informatics
    |March 21, 2024
    PubMed
    概括

    这项研究引入了一种新的AI模型,用于预测重症监护室 (ICU) 的干预,如机械通风和血管压缩器. 可解释模型提高了预测准确性,帮助临床决策和患者安全.

    科学领域:

    • 医疗信息学 医疗信息学
    • 人工智能的人工智能
    • 临床决策支持 临床决策支持

    背景情况:

    • 重症监护室 (ICU) 患者管理需要基于复杂,动态数据的及时干预.
    • 现有的预测模型往往缺乏解释性,阻碍了临床的信任和采用.
    • 对机械通风和血管压缩器等干预措施的准确预测对于患者的治疗结果至关重要.

    研究的目的:

    • 开发和验证一种新的多变量时间序列图形卷积神经网络 (GCN),用于预测ICU干预.
    • 为了提高AI驱动的预测在重症监护设置的可解释性.
    • 提高ICU患者临床决策的准确性和及时性.

    主要方法:

    • 使用MIMIC-III数据库,其中包含现实世界的ICU患者记录.
    • 开发了一个多变量时间序列GCN模型,整合了生理信号,药物数据和患者特征.
    • 进行了相邻矩阵重要性分析,以评估模型可解释性和特征相关性.

    主要成果:

    • 在预测机械通风方面取得了显著的改进:准确度从81.6%增加到91.9%,F1得分从0.524增加到0.606.
    • 对血管压缩剂干预的预测得到了改进:准确度从76.3%上升到82.7%,F1得分从0.509上升到0.619.

    更多相关视频

    Real-Time Monitoring of Neurocritical Patients with Diffuse Optical Spectroscopies
    07:12

    Real-Time Monitoring of Neurocritical Patients with Diffuse Optical Spectroscopies

    Published on: November 19, 2020

    2.1K
    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
    04:09

    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

    Published on: October 10, 2018

    8.2K

    相关实验视频

    Last Updated: Jun 30, 2025

    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
    14:08

    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

    Published on: April 13, 2013

    42.6K
    Real-Time Monitoring of Neurocritical Patients with Diffuse Optical Spectroscopies
    07:12

    Real-Time Monitoring of Neurocritical Patients with Diffuse Optical Spectroscopies

    Published on: November 19, 2020

    2.1K
    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
    04:09

    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

    Published on: October 10, 2018

    8.2K
  • 解释性分析证实了模型依赖临床上有意义的特征.

    • 结论:

    • 拟议的GCN模型为预测关键ICU干预提供了一个强大的,可解释的工具.
    • 这种方法显著优于现有方法,提高了准确性并提供了临床见解.
    • 该研究推进了人工智能驱动的决策支持系统,有望改善患者安全和ICU的结果.