Integrating Natural Language Processing in Medical Information Science for Clinical Text Analysis

Abstract

The rapid digitization of healthcare data has led to an exponential increase in unstructured clinical text, necessitating the integration of Natural Language Processing (NLP) in Medical Information Science. This research explores deep learning-based NLP techniques for clinical text analysis, focusing on Named Entity Recognition (NER), disease classification, adverse drug reaction detection, and clinical text summarization. The study leverages state-of-the-art transformer models such as BioBERT, ClinicalBERT, and GPT-4 Medical, which demonstrate superior performance in extracting key medical entities, classifying diseases, and summarizing electronic health records (EHRs). Experimental results on benchmark datasets such as MIMIC-III, i2b2, and ClinicalTrials.gov show that ClinicalBERT outperforms traditional ML models by achieving an F1-score of 89.9% in NER tasks, while GPT-4 Medical improves EHR summarization efficiency by 40%. By means of automated medical documentation, clinical decision support, and real-time adverse drug event detection which integrates NLP into healthcare systems diagnostic accuracy, physician efficiency, and patient safety are much improved. NLP-driven medical text analysis has great potential to transform clinical procedures and raise patient outcomes despite obstacles like computing costs, data privacy issues, and model interpretability. Improving domain-specific AI models, maximising real-time processing, and guaranteeing ethical AI deployment in healthcare should be the key priorities of next studies.