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Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Updated: Sep 9, 2025

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Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A Review.

Lingchao Mao1, Hairong Wang1, Leland S Hu2

  • 1H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA.

IEEE Transactions on Automation Science and Engineering : a Publication of the IEEE Robotics and Automation Society
|September 2, 2025
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Summary
This summary is machine-generated.

Knowledge-informed machine learning (KIML) enhances cancer diagnosis and prognosis by integrating biomedical knowledge with data-driven models. This approach addresses challenges like limited data and improves model accuracy and interpretability.

Keywords:
Machine learningcancer diagnosisdeep learninghealthcare automationprognosis

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Area of Science:

  • Oncology
  • Artificial Intelligence
  • Bioinformatics

Background:

  • Machine learning (ML) aids cancer diagnosis and prognosis through complex data analysis.
  • ML models face limitations including small sample sizes, high-dimensional data, patient heterogeneity, and interpretability issues.
  • Integrating biomedical knowledge into ML models can enhance accuracy, robustness, and interpretability.

Purpose of the Study:

  • To review state-of-the-art machine learning studies that fuse biomedical knowledge and data for cancer research.
  • To explore knowledge-informed machine learning (KIML) applications in cancer diagnosis and prognosis.
  • To discuss future directions for KIML in advancing cancer research and healthcare automation.

Main Methods:

  • Review of current literature on knowledge-informed machine learning in oncology.
  • Analysis of diverse knowledge representation forms and integration strategies.
  • Examination of concrete examples of KIML in cancer diagnosis and prognosis.

Main Results:

  • KIML demonstrates potential to overcome ML limitations in cancer research.
  • Successful integration of biomedical knowledge improves ML model performance.
  • Diverse strategies exist for representing and integrating knowledge into ML pipelines.

Conclusions:

  • KIML is a promising approach to advance cancer diagnosis and prognosis.
  • Further research into KIML can enhance healthcare automation in oncology.
  • An evolving online resource is available to support KIML research in cancer.