NLP and AI for Public Health Intelligence: Automating Disease Surveillance from Unstructured Data DOI

Vijayalaxmi Methuku

Published: March 28, 2025

Public health surveillance is crucial for early disease detection, outbreak prediction, and epidemic response. However, traditional systems primarily rely on structured clinical data, limiting their capacity to capture emerging threats from diverse unstructured sources. This study explores the integration of Natural Language Processing (NLP) Artificial Intelligence (AI) automate by analyzing including electronic records (EHRs), social media posts, news reports, online forums. Leveraging state-of-the-art NLP techniques—such as transformer-based language models, named entity recognition (NER), sentiment analysis, topic modeling—an AI-driven framework proposed process, classify, extract epidemiological insights vast text streams in real time. The integrates multilingual data processing, anomaly geospatial trend analysis enhance warning capabilities healthcare authorities. Its effectiveness evaluated using benchmark datasets, such BioCaster Global Health Monitor, real-world case studies infectious outbreaks, demonstrating significant improvements detection speed accuracy. findings highlight transformative role AI advancing public intelligence, improving scalability, enabling proactive intervention strategies.

Language: Английский

NLP and AI for Public Health Intelligence: Automating Disease Surveillance from Unstructured Data DOI

Vijayalaxmi Methuku

Published: March 28, 2025

Public health surveillance is crucial for early disease detection, outbreak prediction, and epidemic response. However, traditional systems primarily rely on structured clinical data, limiting their capacity to capture emerging threats from diverse unstructured sources. This study explores the integration of Natural Language Processing (NLP) Artificial Intelligence (AI) automate by analyzing including electronic records (EHRs), social media posts, news reports, online forums. Leveraging state-of-the-art NLP techniques—such as transformer-based language models, named entity recognition (NER), sentiment analysis, topic modeling—an AI-driven framework proposed process, classify, extract epidemiological insights vast text streams in real time. The integrates multilingual data processing, anomaly geospatial trend analysis enhance warning capabilities healthcare authorities. Its effectiveness evaluated using benchmark datasets, such BioCaster Global Health Monitor, real-world case studies infectious outbreaks, demonstrating significant improvements detection speed accuracy. findings highlight transformative role AI advancing public intelligence, improving scalability, enabling proactive intervention strategies.

Language: Английский

Citations

0