Optimizing Rectal Cancer Patient Care: Dworak TRG Prediction via Bayesian Evolutionary Fourier-Domain Random Subspace Forest-Reference-Cited by-同舟云学术

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Optimizing Rectal Cancer Patient Care: Dworak TRG Prediction via Bayesian Evolutionary Fourier-Domain Random Subspace Forest

Published:2024 Issue: Volume:73 Page:1-10
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Raets Camille¹^ORCID,Aisati Chaïmae El²^ORCID,Rifi Amir L.¹^ORCID,De Ridder Mark²^ORCID,Putman Koen³^ORCID,De Mey Johan⁴^ORCID,Sermeus Alexandra²^ORCID,Barbé Kurt¹^ORCID

Affiliation:

1. Biostatistics and Medical Informatics Research Group (BISI), Vrije Universiteit Brussel, Brussels, Belgium

2. Department of Radiotherapy, Universitair Ziekenhuis Brussel (UZB), Brussels, Belgium

3. Interuniversity Centre for Health Economics Research (I-CHER), Vrije Universiteit Brussel, Brussels, Belgium

4. Department of Radiology, Universitair Ziekenhuis Brussel, Brussels, Belgium

Funder

Members of the Smart-Qi Project at Vrije Universiteit Brussel

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/19/10367905/10440192.pdf?arnumber=10440192

Reference28 articles.

1. Colorectal cancer

2. Machine learning applications in cancer prognosis and prediction

3. Epidemiology of colorectal cancer: incidence, mortality, survival, and risk factors

4. The association of bowel function, participation in life activities, and quality of life in rectal cancer survivors

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