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

Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

408
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
408
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

96
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
96
Causality in Epidemiology01:21

Causality in Epidemiology

463
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
463
Classification of Illness01:17

Classification of Illness

7.6K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
7.6K
Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

597
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...
597
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

73
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
73

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

Lung-Protective Ventilation Strategies for Relief from Ventilator-Associated Lung Injury in Patients Undergoing Craniotomy: A Bicenter Randomized, Parallel, and Controlled Trial.

Oxidative medicine and cellular longevity·2017
Same author

Electrochemical Oxidation of EDTA in Nuclear Wastewater Using Platinum Supported on Activated Carbon Fibers.

International journal of environmental research and public health·2017
Same author

Novel biomimetic enzyme for sensitive detection of superoxide anions.

Talanta·2017
Same author

The Expression of Formyl Peptide Receptor 1 is Correlated with Tumor Invasion of Human Colorectal Cancer.

Scientific reports·2017
Same author

An excited state underlies gene regulation of a transcriptional riboswitch.

Nature chemical biology·2017
Same author

(-)-Epigallocatechin-3-gallate attenuates myocardial injury induced by ischemia/reperfusion in diabetic rats and in H9c2 cells under hyperglycemic conditions.

International journal of molecular medicine·2017

相关实验视频

Updated: Jul 17, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.7K

使用电子健康记录图形模型进行并发症网络分析.

Bo Zhao1, Sarah Huepenbecker2, Gen Zhu1

  • 1Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center, Houston, TX, United States.

Frontiers in big data
|September 4, 2023
PubMed
概括

这项研究开发了一种机器学习方法,用于绘制重症监护室 (CCU) 患者共同疾病的地图,其表现优于传统方法. 识别的并发症网络有助于更快的诊断,并降低患者的死亡率.

关键词:
共同疾病网络分析分析.临床重症监护病房的重症监护病房.电子健康记录是电子健康记录.图形建模方法是一种图形建模方法.机器学习是机器学习.

更多相关视频

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.4K
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

7.1K

相关实验视频

Last Updated: Jul 17, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.7K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.4K
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

7.1K

科学领域:

  • 关键护理医学 关键护理医学
  • 计算生物学是一种计算生物学.
  • 医疗信息学医学信息学

背景情况:

  • 了解重症监护单位 (CCU) 的并发症网络对于及时诊断和改善患者结果至关重要.
  • 分析疾病关系的现有方法可能无法完全捕捉复杂的并发症模式.

研究的目的:

  • 通过一种新的机器学习 (图形建模) 方法,识别CCU患者之间的并发症网络.
  • 在模拟中,将机器学习方法的性能与传统的对对方法进行比较.

主要方法:

  • 一项采用电子健康记录数据的横截面研究,该数据来自密集护理-3 (MIMIC-3) 数据集 (2001-2012) 的医疗信息市场.
  • 应用图形建模方法来分析46,511名CCU患者中的654个诊断类别.
  • 医学专业人员对已识别的关联进行验证,并通过模拟与传统的配对方法进行比较.

主要成果:

  • 图形建模方法在510个诊断类别中确定了2,806个关联,专家验证证实了医疗一致性.
  • 关键的发现包括"精神药物中毒"和"镇静剂意外中毒" (logOR 8.16) 等强烈的关联.
  • 诊断"流体,电解质和酸平衡障碍"是最相关的 (63个关联),该方法在模拟中表现优于传统方法,将诊断分为14个模块化类别.

结论:

  • 开发的图形建模方法有效地推断出CCU患者的临床相关的并发症网络.
  • 这种数据驱动的方法在模拟研究中显示出与传统方法相比更高的性能.
  • 已确定的并发症网络有潜力显著帮助临床医生更快地诊断和减少CCU设置中的错误诊断.