GPU-Accelerated Predictive Modeling for Personalized Medicine

EasyChair Preprint 13903

Abstract

Personalized medicine is transforming healthcare by tailoring treatment strategies to individual patients based on their genetic, environmental, and lifestyle factors. Central to this transformation is predictive modeling, which leverages vast amounts of data to forecast disease risk, treatment response, and patient outcomes. However, the computational demands of such modeling are substantial, often involving complex algorithms and large datasets. This paper explores the utilization of GPU (Graphics Processing Unit) acceleration to enhance the performance and efficiency of predictive modeling in personalized medicine. By offloading computationally intensive tasks to GPUs, we achieve significant speed-ups in data processing and model training times, enabling real-time predictions and more accurate patient-specific insights. We illustrate the advantages of GPU-accelerated predictive modeling through case studies in oncology, cardiology, and pharmacogenomics, demonstrating improvements in prediction accuracy, scalability, and overall computational efficiency. This approach not only enhances the feasibility of implementing personalized medicine on a broad scale but also paves the way for more responsive and adaptive healthcare systems, ultimately leading to better patient outcomes and optimized therapeutic interventions.

Keyphrases: Bioinformatic algorithms, Computational genomics, Deep learning in bioinformatics, GPU-accelerated machine learning, GPU-based bioinformatics, High Performance Computing

@booklet{EasyChair:13903,
  author    = {Abey Litty},
  title     = {GPU-Accelerated Predictive Modeling for Personalized Medicine},
  howpublished = {EasyChair Preprint 13903},
  year      = {EasyChair, 2024}}