Predicting venous thromboembolism (VTE) risk in cancer patients using machine learning.

Journal: Health care science
Published Date: Jul 13, 2023

Abstract

BACKGROUND: The association between cancer and venous thromboembolism (VTE) is well-established with cancer patients accounting for approximately 20% of all VTE incidents. In this paper, we have performed a comparison of machine learning (ML) methods to traditional clinical scoring models for predicting the occurrence of VTE in a cancer patient population, identified important features (clinical biomarkers) for ML model predictions, and examined how different approaches to reducing the number of features used in the model impact model performance.

Authors

  • Samir Khan Townsley
    Department of Electrical and Computer Engineering University of California Davis California USA.
  • Debraj Basu
    Department of Electrical and Computer Engineering University of California Davis California USA.
  • Jayneel Vora
    Department of Computer Science University of California Davis California USA.
  • Ted Wun
    School of Medicine, Davis Health University of California Sacramento California USA.
  • Chen-Nee Chuah
    Department of Electrical and Computer Engineering University of California Davis California USA.
  • Prabhu R V Shankar
    School of Medicine, Davis Health University of California Sacramento California USA.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals