NLP-Based Medical Text Mining for Early Detection of Disease Outbreaks and Public Health Trends
DOI:
https://doi.org/10.66021/pakmcr1127Keywords:
Natural Language Processing, Medical Text Mining, Event-Based Surveillance, Disease Outbreak Detection, Biobert, Clinicalbert, Syndromic Surveillance, Electronic Health Records, Public Health Intelligence, Deep Learning In EpidemiologyAbstract
Natural Language Processing (NLP) and medical text mining have emerged as transformative tools for shifting public health surveillance from reactive, indicator-based systems to proactive, event-based intelligence. This review explores how advanced NLP techniques including Named Entity Recognition (NER), relationship extraction, text classification, and sentiment analysis enable the real-time extraction of actionable insights from unstructured data sources such as electronic health records (EHRs), clinical narratives, news reports, and social media. Domain-adapted models like BioBERT, ClinicalBERT, and BERTweet, combined with deep learning architectures (Bi-LSTM with multi-head attention achieving 98.25% accuracy), facilitate early detection of disease outbreaks, syndromic surveillance, and trend monitoring. Global frameworks such as HealthMap and WHO’s EIOS demonstrate the practical impact of these technologies. While challenges including data noise, cross-lingual privacy risks, and the digital divide persist, multimodal fusion and AI-driven systems offer significant potential for improving epidemic preparedness, response speed, and public health decision-making in an increasingly interconnected world.
