Large-Scale Medical Records Analysis by AI-Driven Method in Healthcare Consumer Electronics

Abstract

Integrating AI-driven method in healthcare consumer electronics has opened up new possibilities for large-scale medical records analysis. We introduce a novel approach harnessing the capabilities of AI-driven method to extract valuable insights from extensive medical datasets, facilitating more efficient and precise healthcare decision-making processes. The proposed model integrates a recurrent layer founded on bidirectional long short-term memory with a transformer layer incorporating a multi-channel self-attention mechanism. We introduce a weight-based auxiliary training promotion method to enhance the model’s performance and robustness further. Experiments conducted on two widely-used benchmark datasets, namely the 2010 i2b2/VA and SemEval 2013 DDI datasets, illustrate the proposed model’s superior performance compared to state-of-the-art methods. Our model achieves the highest precision, recall, and F1 scores on both datasets, surpassing the baseline approaches by a considerable margin. Moreover, the proposed model exhibits faster convergence and improved generalization ability, making it more efficient and practical for real-world applications. Integrating AI-driven method in healthcare consumer electronics holds great promise for revolutionizing how medical records are analyzed and utilized, ultimately leading to improved patient outcomes and personalized healthcare solutions.