@article{jcsa20241213,
author={{Reynoso-Aguirre, Pablo Eliseo and Flores-P¨Śrez, Pedro},
title={Facing the Clinical Trial Annotation Problem on Breast Cancer: Natural Language Processing & Machine Learning Models Selection},
journal={Journal of Computer Sciences and Applications},
volume={12},
number={1},
pages={17--24},
year={2024},
url={https://pubs.sciepub.com/jcsa/12/1/3},
issn={2328-725X},
abstract={<b>  </b>Clinical trial classification problem (CTCP) is one of the cutting-edge real-life applications in biomedical informatics, especially in the domain considered in this paper, namely breast cancer. The task consists in the development of models able to discriminate patientĄŻs eligibility profile at breast cancer trials based on performance status (PS) labels. The task has gained relevance at medical research and practice in the framework of decision support systems. Besides, the task has been considered a meaningful instrument for an accurate selection of participants at experimentations resulting in no health-behavioral drug side effects on participants.},
doi={10.12691/jcsa-12-1-3}
publisher={Science and Education Publishing}
}