Leveraging BERT and LSTM for Enhanced Natural Language Processing in Clinical Data Analysis

Abstract

This research paper explores the integration of Bidirectional Encoder Representations from Transformers (BERT) and Long Short-Term Memory (LSTM) networks to enhance natural language processing (NLP) in clinical data analysis. The study acknowledges the complexity and specificity inherent in clinical data, which pose significant challenges for traditional NLP methods. BERT, with its contextual understanding capabilities, provides a robust architecture for understanding the nuanced semantics of medical text, while LSTM networks offer a powerful mechanism for capturing sequential dependencies and contextual history. By combining these two approaches, the research aims to develop a hybrid model that efficiently processes and interprets clinical narratives, improving tasks such as named entity recognition, sentiment analysis, and relationship extraction. The paper presents a comprehensive evaluation of the proposed model on benchmark clinical datasets, demonstrating its superiority over existing models in terms of accuracy, precision, and recall. Additionally, the study highlights potential applications in electronic health records (EHR) management and clinical decision support systems, illustrating how this approach can facilitate better patient care through more accurate and insightful data interpretation. The findings suggest that leveraging the strengths of BERT and LSTM not only enhances the processing of complex clinical narratives but also opens pathways for future research in NLP applications across various healthcare domains.