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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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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: Jul 25, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Predicting Overall Survival in METABRIC Cohort Using Machine Learning.

Afroz Banu1, Rayyan Ahmed2, Saleh Musleh2

  • 1College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar.

Studies in Health Technology and Informatics
|June 30, 2023
PubMed
Summary

This study used machine learning to predict survival in triple-negative breast cancer (TNBC) patients. It identified key clinical and genetic factors linked to better outcomes, improving upon existing methods.

Keywords:
Breast CancerMachine Learning

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Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Triple-negative breast cancer (TNBC) is an aggressive subtype with high mortality and relapse rates.
  • Genetic variations influence TNBC patient outcomes and treatment responses.
  • Predictive models are needed to identify factors associated with survival.

Purpose of the Study:

  • To predict overall survival in TNBC patients using supervised machine learning.
  • To identify significant clinical and genetic features correlating with improved survival.
  • To discover biological pathways linked to key survival-associated genes.

Main Methods:

  • Utilized supervised machine learning algorithms on the METABRIC cohort data.
  • Focused on predicting overall survival for triple-negative breast cancer patients.
  • Identified and analyzed important clinical and genetic features.

Main Results:

  • Achieved a higher Concordance index compared to existing state-of-the-art methods.
  • Identified key clinical and genetic features associated with better patient survival.
  • Discovered relevant biological pathways connected to the top predictive genes.

Conclusions:

  • Machine learning can effectively predict survival in TNBC patients.
  • Specific clinical and genetic features are crucial indicators of better outcomes.
  • The identified pathways offer insights into TNBC biology and potential therapeutic targets.