THE ROLE OF NATURAL LANGUAGE PROCESSING (NLP) IN CLINICAL DATA ANALYSIS

Purpose: The increasing volume of unstructured clinical data presents challenges in healthcare analytics, necessitating advanced computational methods for meaningful data extraction. This study investigates the role of natural language processing (NLP) techniques, specifically deep learning-based models, in improving the accuracy and efficiency of clinical data analysis. The primary objective is to evaluate the performance of an NLP pipeline in extracting structured information from electronic health records (EHRs) and assess its potential impact on clinical decision-making. Methods: A retrospective dataset of 25,000 de-identified EHRs from a tertiary care hospital was analyzed. The NLP pipeline integrated advanced preprocessing techniques, a fine-tuned BioBERT model for named entity recognition, and a relation extraction framework for mapping entities to clinical ontologies. Model performance was evaluated using precision, recall, F1-score, and receiver operating characteristic (ROC) analysis. Statistical tests were conducted to assess variations in entity recognition across different clinical domains. Results: The proposed model achieved a precision of 0.92, recall of 0.89, and an F1-score of 0.90, with an AUC of 0.95. The pipeline effectively processed unstructured clinical texts, significantly reducing manual effort and improving information extraction accuracy. Error analysis identified common misclassifications related to ambiguous abbreviations and overlapping terminologies. Conclusion: The findings support the integration of NLP-driven analytics in healthcare, demonstrating its potential to enhance clinical decision support and streamline data management. Future research should focus on validating these results in multi-center studies and refining models to address domain-specific linguistic challenges.