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Related Concept Videos

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.
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Introduction To Survival Analysis01:18

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Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
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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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Survival Curves01:18

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Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
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Kaplan-Meier Approach01:24

Kaplan-Meier Approach

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The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
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Comparing the Survival Analysis of Two or More Groups01:20

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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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SurvNAM: The machine learning survival model explanation.

Lev V Utkin1, Egor D Satyukov1, Andrei V Konstantinov1

  • 1Peter the Great St.Petersburg Polytechnic University, St.Petersburg, Russia.

Neural Networks : the Official Journal of the International Neural Network Society
|January 7, 2022
PubMed
Summary

SurvNAM explains black-box survival models using a Neural Additive Model (NAM) extension. This method provides both local and global explanations for survival analysis predictions, enhancing model interpretability.

Keywords:
Explainable AIShortcut connectionSurvival analysisThe Cox modelThe lasso method

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Area of Science:

  • Machine Learning
  • Survival Analysis
  • Explainable AI

Background:

  • Black-box machine learning models in survival analysis lack interpretability.
  • Explaining predictions is crucial for trust and validation in medical and financial applications.

Purpose of the Study:

  • To introduce SurvNAM, an extension of Neural Additive Models (NAM), for explaining black-box survival models.
  • To enable both local and global interpretability of survival model predictions.

Main Methods:

  • Adapting NAM for survival analysis using a specialized expected loss function.
  • Approximating Cox proportional hazards models with Generalized Additive Models (GAM).
  • Implementing modifications including Lasso regularization and shortcut connections for GAM function representation.

Main Results:

  • SurvNAM effectively provides local explanations by generating synthetic data around specific examples.
  • Global explanations are achieved by utilizing the entire training dataset.
  • Numerical experiments demonstrate the efficiency and effectiveness of the proposed SurvNAM method.

Conclusions:

  • SurvNAM offers a viable solution for interpreting complex machine learning survival models.
  • The method enhances transparency in survival analysis, facilitating better decision-making.
  • Further modifications improve the performance and representation of GAM components within SurvNAM.