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Related Concept Videos

Survival Tree01:19

Survival Tree

105
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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Survival Curves01:18

Survival Curves

192
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.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
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Introduction To Survival Analysis01:18

Introduction To Survival Analysis

272
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.
The primary goal of survival analysis is to estimate survival time—the time...
272
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

153
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.
153
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

369
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...
369
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

178
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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Related Experiment Video

Updated: Jul 15, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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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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GNN-surv: Discrete-Time Survival Prediction Using Graph Neural Networks.

So Yeon Kim1,2

  • 1Department of Artificial Intelligence, Ajou University, Suwon 16499, Republic of Korea.

Bioengineering (Basel, Switzerland)
|September 28, 2023
PubMed
Summary

Graph Neural Networks (GNNs) improve cancer survival predictions by analyzing patient similarity networks. These GNN-surv models offer enhanced accuracy for personalized treatment planning in oncology.

Keywords:
Graph Neural Networksdiscrete survival modelpatient similarity networksurvival predictiontime-to-event prediction

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Area of Science:

  • Oncology
  • Bioinformatics
  • Machine Learning

Background:

  • Accurate survival prediction is crucial for patient prognosis and personalized cancer treatment.
  • Traditional models can be enhanced by integrating patient similarity networks to capture complex data patterns.
  • Graph Neural Networks (GNNs) offer a powerful approach for leveraging network structures in data.

Purpose of the Study:

  • To develop and evaluate Graph Neural Network-based survival prediction models (GNN-surv) for enhanced accuracy.
  • To leverage patient similarity networks constructed from genomic and clinical data for improved survival analysis.
  • To assess the performance of GNN-surv models against traditional models in urologic cancer datasets.

Main Methods:

  • Construction of patient similarity networks using genomic and clinical data from cancer patients.
  • Training and evaluation of various GNN models integrated with Logistic Hazard and Probability Mass Function (PMF) survival models.
  • Comparison of GNN-surv model performance against Multilayer Perceptron (MLP) models using time-dependent concordance index and integrated Brier score.

Main Results:

  • GNN-surv models significantly outperformed traditional MLP models in survival prediction on BLCA and KIRC datasets.
  • Performance improvements included up to 14.6% and 7.9% increases in the time-dependent concordance index.
  • Reductions in the integrated Brier score were observed, reaching 26.7% and 24.1% for BLCA and KIRC, respectively.
  • Models demonstrated robustness across varying graph construction hyperparameters and effectiveness across different GNN architectures.

Conclusions:

  • GNN-surv models provide a superior approach for discrete-time survival prediction by effectively utilizing patient similarity networks.
  • The enhanced accuracy and robustness of GNN-surv models offer a valuable tool for clinicians in oncology and personalized medicine.
  • The adaptability of these models suggests broad applicability across different cancer types and potential for integration with other survival models or data modalities.