A human-in-the-loop based Bayesian network approach to improve imbalanced radiation outcomes prediction for hepatocellular cancer patients with stereotactic body radiotherapy

A human-in-the-loop based Bayesian network approach to improve imbalanced radiation outcomes prediction for hepatocellular cancer patients with stereotactic body radiotherapy

BackgroundImbalanced outcome is one of common characteristics of oncology datasets. Current machine learning approaches have limitation in learning from such datasets. Here, we propose to resolve this problem by utilizing a human-in-the-loop (HITL) approach, which we hypothesize will also lead to mo...

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Bibliographic Details
Main Authors: Yi Luo, Kyle C. Cuneo, Theodore S. Lawrence, Martha M. Matuszak, Laura A. Dawson, Dipesh Niraula, Randall K. Ten Haken, Issam El Naqa
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-12-01
Series:Frontiers in Oncology
Subjects:
accuracy and explainability
Bayesian networks
human-in-the-loop
hepatocellular cancer
outcome prediction
stereotactic body radiotherapy
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2022.1061024/full

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https://www.frontiersin.org/articles/10.3389/fonc.2022.1061024/full

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