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

BACKGROUND: Imbalanced 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...

Descripción completa

Detalles Bibliográficos
Autores principales: Luo, Yi, Cuneo, Kyle C., Lawrence, Theodore S., Matuszak, Martha M., Dawson, Laura A., Niraula, Dipesh, Ten Haken, Randall K., El Naqa, Issam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Oncology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9782976/
https://www.ncbi.nlm.nih.gov/pubmed/36568208
http://dx.doi.org/10.3389/fonc.2022.1061024

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9782976/
https://www.ncbi.nlm.nih.gov/pubmed/36568208
http://dx.doi.org/10.3389/fonc.2022.1061024

Ejemplares similares