Accelerating Biomedical Text Mining with GPU-Enhanced Machine Learning

EasyChair Preprint 14200

Abstract

Biomedical text mining has emerged as a critical tool in extracting valuable insights from the vast and rapidly expanding biomedical literature. Traditional methods of text mining, while effective, often struggle to keep pace with the growing volume of data, leading to bottlenecks in information retrieval and analysis. This study explores the application of GPU-enhanced machine learning techniques to accelerate biomedical text mining processes, aiming to improve the efficiency and accuracy of information extraction.

Leveraging the parallel processing power of GPUs, we developed and implemented advanced machine learning models specifically designed for large-scale text mining tasks. These models were evaluated on various biomedical corpora to assess their performance in terms of speed, scalability, and precision. Our results demonstrate a significant reduction in processing time compared to CPU-based approaches, without compromising the quality of the extracted information.

Furthermore, the integration of GPU acceleration allowed for the deployment of more complex and deeper neural network architectures, which improved the system's ability to understand and interpret nuanced biomedical terminology and concepts. This advancement has the potential to transform how researchers and practitioners access and utilize biomedical knowledge, enabling more rapid advancements in medical research and clinical practice.

Keyphrases: Biomedical, machine learning, text mining

@booklet{EasyChair:14200,
  author    = {Abill Robert},
  title     = {Accelerating Biomedical Text Mining with GPU-Enhanced Machine Learning},
  howpublished = {EasyChair Preprint 14200},
  year      = {EasyChair, 2024}}