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Flexible Risk Evidence Combination Rules in Breast Cancer Precision Therapy.

Michael KennRudolf KarchChristian F SingerGeorg DorffnerWolfgang Schreiner
Published in: Journal of personalized medicine (2023)
Evidence theory by Dempster-Shafer for determination of hormone receptor status in breast cancer samples was introduced in our previous paper. One major topic pointed out here is the link between pieces of evidence found from different origins. In this paper the challenge of selecting appropriate ways of fusing evidence, depending on the type and quality of data involved is addressed. A parameterized family of evidence combination rules, covering the full range of potential needs, from emphasizing discrepancies in the measurements to aspiring accordance, is covered. The consequences for real patient samples are shown by modeling different decision strategies.
Keyphrases
  • machine learning
  • stem cells
  • risk assessment
  • deep learning
  • mass spectrometry
  • electronic health record
  • quality improvement
  • cell therapy
  • smoking cessation
  • solid state
  • tandem mass spectrometry