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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
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Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
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 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
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Variational Disentanglement for Rare Event Modeling.

Zidi Xiu1, Chenyang Tao1, Michael Gao1

  • 1Duke University.

Arxiv
|September 30, 2020
PubMed
Summary

This study introduces a new machine learning method to improve rare event prediction in imbalanced datasets, crucial for healthcare risk assessment and clinical decision support.

Area of Science:

  • Machine Learning
  • Healthcare Data Analysis
  • Biostatistics

Background:

  • Healthcare data and machine learning advances offer opportunities for clinical decision support.
  • Imbalanced classification, common in healthcare risk prediction, presents significant challenges due to low prevalence of target conditions.
  • Existing methods struggle with accurately identifying rare events in large, imbalanced datasets.

Approach:

  • Proposes a variational disentanglement approach for semi-parametric learning from rare events.
  • Utilizes extreme-distribution behavior in a latent space to extract information from low-prevalence events.
  • Develops a robust prediction model combining generalized additive models and isotonic neural networks.

Key Points:

  • The method effectively learns from rare events in heavily imbalanced classification problems.

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  • Demonstrates superior performance on synthetic data and real-world healthcare datasets.
  • Successfully applied to mortality prediction in a COVID-19 cohort, outperforming current alternatives.

    • Conclusions:

    • The proposed variational disentanglement approach offers a powerful solution for rare event prediction in imbalanced datasets.
    • This method enhances the accuracy and reliability of clinical decision support systems.
    • Significant implications for improving risk prediction models in various medical applications.