Advancing Predictive Healthcare: A Systematic Review of Transformer Models in Electronic Health Records

Abstract

This systematic study seeks to evaluate the use and impact of transformer models in the healthcare domain, with a particular emphasis on their usefulness in tackling key medical difficulties and performing critical natural language processing (NLP) functions. The research questions focus on how these models can improve clinical decision-making through information extraction and predictive analytics. Our findings show that transformer models, especially in applications like named entity recognition (NER) and clinical data analysis, greatly increase the accuracy and efficiency of processing unstructured data. Notably, case studies demonstrated a 30% boost in entity recognition accuracy in clinical notes and a 90% detection rate for malignancies in medical imaging. These contributions emphasize the revolutionary potential of transformer models in healthcare, and therefore their importance in enhancing resource management and patient outcomes. Furthermore, this paper emphasizes significant obstacles, such as the reliance on restricted datasets and the need for data format standardization, and provides a road map for future research to improve the applicability and performance of these models in real-world clinical settings.