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Survival Tree01:19

Survival Tree

138
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.
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Randomized Experiments01:13

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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Margin of Error01:27

Margin of Error

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The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
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Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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Goodness-of-Fit Test01:16

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The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
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Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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Related Experiment Video

Updated: Aug 29, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

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Towards Adversarial Robustness with Early Exit Ensembles.

Lorena Qendro, Cecilia Mascolo

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |September 10, 2022
    PubMed
    Summary

    This study introduces a novel method to protect deep learning models in healthcare from adversarial attacks using early exit neural networks. This approach enhances model accuracy and security without costly re-training.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Healthcare Technology

    Background:

    • Deep learning models in healthcare are vulnerable to adversarial attacks, compromising security and privacy.
    • Existing mitigation strategies are often reactive and computationally expensive, requiring frequent re-training.
    • The growing sophistication of adversarial attacks necessitates robust and efficient defense mechanisms.

    Purpose of the Study:

    • To propose a novel adversarial mitigation technique for biosignal classification tasks.
    • To enhance the security and reliability of deep learning models in health applications.
    • To offer a defense against adversarial examples without re-training.

    Main Methods:

    • Utilized early exit neural networks, interpreted as ensembles of weight-sharing subnetworks.
    • Applied the proposed technique to state-of-the-art deep learning models for biosignal classification.
    • Evaluated robustness against white-box and universal adversarial attacks.

    Main Results:

    • Early exit ensembles demonstrated generalizable robustness against diverse adversarial attacks.
    • Achieved accuracy improvements of up to 60 percentage points in vulnerable deep learning models.
    • Provided adversarial mitigation comparable to adversarial training, without re-training burdens.

    Conclusions:

    • Early exit neural network ensembles offer a computationally efficient and effective defense against adversarial attacks in biosignal classification.
    • The proposed method enhances the security and practical deployment of deep learning in clinical domains.
    • This technique provides a proactive and adaptable solution to evolving adversarial threats in healthcare AI.