Recent Progress in the Application of Natural Language Processing for Infectious Disease Detection and Surveillance in Clinical Data
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Updated: Nov 5, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
Published on: September 20, 2018
David Martinez1, Vlada Rozova2,3,4, Curtis Murray1,3
1School of Computing Technologies, RMIT University, Melbourne, Australia.
Summary:
OBJECTIVES: : In this review, we examine the application of natural language processing (NLP) methods to clinical documents from electronic health records for the detection and tracking of infectious diseases.
Summary:
METHODS: : We searched academic databases using artificial intelligence-assisted tools to capture all relevant literature from 2022 onwards.
Summary:
RESULTS: : Our review covers the large body of research around COVID-19, other human infectious diseases, new and emerging diseases, veterinary medicine, general solutions for clinical NLP, and resources and datasets.
Summary:
CONCLUSION: : We observe that large language models are being successfully used in recent work for a variety of tasks, including named entity recognition, relation extraction, and text classification.
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