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Updated: Feb 24, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

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在基于电子健康记录的机器学习预测模型中调整共变量错误分类

Shuang Yang1, Yonghui Wu1, Mei Liu1

  • 1Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, Florida, USA.

AMIA ... Annual Symposium proceedings. AMIA Symposium
|February 23, 2026
PubMed
概括

这项研究引入了纠正电子健康记录数据错误的方法,改善了肺癌查的预测模型. 调整后的模型显示出比未调整的模型更好的准确性,减少了在没有手动审查的情况下的偏差.

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Confounding in Epidemiological Studies

Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
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Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
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Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
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Classification of Illness

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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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:
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