Leveraging Bidirectional Encoder Representations from Transformers (BERT) and Latent Dirichlet Allocation (LDA) for Enhanced Natural Language Processing in Electronic Health Record Data Mining

Abstract

This paper presents a novel approach to enhancing natural language processing (NLP) in electronic health record (EHR) data mining by integrating Bidirectional Encoder Representations from Transformers (BERT) with Latent Dirichlet Allocation (LDA). EHRs contain vast amounts of unstructured text, posing significant challenges for effective data extraction and analysis. We propose a synergistic model combining the deep contextual understanding of BERT with the topic modeling capabilities of LDA to improve the accuracy and depth of information retrieval from EHRs. The model leverages BERT’s ability to capture intricate semantic relationships and contextual nuances within clinical texts, while LDA provides a robust framework for extracting thematic patterns. Our experimental evaluation, conducted on a large corpus of de-identified EHR data, demonstrates significant improvements in both precision and recall compared to traditional NLP techniques. We report enhancements in identifying patient-related information such as symptoms, medical history, and treatment plans, thereby supporting more informed clinical decision-making. This approach not only improves text mining performance but also offers scalable solutions adaptable to various EHR systems. The integration of BERT and LDA signifies a promising step forward in the application of advanced NLP methodologies to healthcare data analytics, ultimately contributing to the advancement of personalized medicine and healthcare delivery.