Leveraging Bayesian networks and information theory to learn risk factors for breast cancer metastasis

Abstract

Even though we have established a few risk factors for metastatic breast cancer (MBC) through epidemiologic studies, these risk factors have not proven to be effective in predicting an individual’s risk of developing metastasis. Therefore, identifying critical risk factors for MBC continues to be a major research imperative, and one which can lead to advances in breast cancer clinical care. The objective of this research is to leverage Bayesian Networks (BN) and information theory to identify key risk factors for breast cancer metastasis from data.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jul 10, 2020
Source ID
10.1186/s12859-020-03638-8

Entities

People

  • Adam Brufsky
  • Alan Wells
  • Darshan Shetty
  • Kahmil Shajihan
  • Richard E. Neapolitan
  • Xia Jiang

Organizations

  • United States Department of Defense
  • United States National Library of Medicine

Tags

Readers

  • Neural Network Machine Learning.
  • Oncology (Cancer Research).
  • Women's Health and Cancer Risk Research: African American Women and Pregnancy Outcomes.

Technology Areas

  • AI & ML