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相关概念视频

Methods of Documentation VII: EMR01:30

Methods of Documentation VII: EMR

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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...
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Time-Series Graph00:54

Time-Series Graph

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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...
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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

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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...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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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.
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Electrocardiogram01:29

Electrocardiogram

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An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...
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相关实验视频

Updated: Jun 11, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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预测建模与时间图形表示在电子健康记录上的预测建模.

Jiayuan Chen1, Changchang Yin1, Yuanlong Wang1

  • 1The Ohio State University.

IJCAI : proceedings of the conference
|October 3, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的时间异质图和时间图转换器 (TRANS),以有效地表示患者电子健康记录 (EHR). TRANS捕获了时间和结构的EHR信息,实现了最先进的预测性能.

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科学领域:

  • 医疗保健中的人工智能
  • 机器学习用于医疗信息学
  • 深度学习用于预测建模.

背景情况:

  • 电子健康记录 (EHR) 对于基于深度学习的医疗预测至关重要.
  • 现有的方法很难有效地整合时间和结构EHR信息.
  • 序列模型捕获时间,但错过了结构数据;图形模型捕获结构,但错过了时间动态.

研究的目的:

  • 开发一种新型患者电子病历表征,将时间和结构信息整合在一起.
  • 引入一个时间图形转换器 (TRANS) 进行增强的EHR分析.
  • 提高医疗保健中基于深度学习的预测模型的准确性.

主要方法:

  • 模拟患者的EHR作为一个时间异质图,带有访问和医疗事件节点.
  • 开发了TRANS,结合了时间边缘特征,位置编码和图形卷积.
  • 综合结构化信息传播和健康状况变化的时间感知节点.

主要成果:

  • 拟议的时间异质图有效地捕捉了时间和结构EHR数据.
  • 在整合多样化的EHR信息方面,TRANS表现出卓越的表现.
  • 在三个真实世界数据集上进行了广泛的实验,证实了最先进的结果.

结论:

  • 新的时间异质图和TRANS模型在EHR表示中提供了显著的进步.
  • 这种方法增强了医疗预测的深度学习模型能力.
  • TRANS提供了一个强大的框架,可以利用复杂的EHR数据.