Cargando…
Quantum deep reinforcement learning for clinical decision support in oncology: application to adaptive radiotherapy
Subtle differences in a patient’s genetics and physiology may alter radiotherapy (RT) treatment responses, motivating the need for a more personalized treatment plan. Accordingly, we have developed a novel quantum deep reinforcement learning (qDRL) framework for clinical decision support that can es...
Autores principales: | Niraula, Dipesh, Jamaluddin, Jamalina, Matuszak, Martha M., Haken, Randall K. Ten, Naqa, Issam El |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8651664/ https://www.ncbi.nlm.nih.gov/pubmed/34876609 http://dx.doi.org/10.1038/s41598-021-02910-y |
Ejemplares similares
-
Author Correction: Quantum deep reinforcement learning for clinical decision support in oncology: application to adaptive radiotherapy
por: Niraula, Dipesh, et al.
Publicado: (2023) -
A clinical decision support system for AI-assisted decision-making in response-adaptive radiotherapy (ARCliDS)
por: Niraula, Dipesh, et al.
Publicado: (2023) -
A human-in-the-loop based Bayesian network approach to improve imbalanced radiation outcomes prediction for hepatocellular cancer patients with stereotactic body radiotherapy
por: Luo, Yi, et al.
Publicado: (2022) -
Current status and future developments in predicting outcomes in radiation oncology
por: Niraula, Dipesh, et al.
Publicado: (2022) -
The Role of Machine Learning in Knowledge-Based Response-Adapted Radiotherapy
por: Tseng, Huan-Hsin, et al.
Publicado: (2018)