Recent Progress in the Application of Natural Language Processing for Infectious Disease Detection and Surveillance in Clinical Data
David Martinez, Vlada Rozova, Curtis Murray, Wei Han, Anna Khanina, Lawrence Cavedon, Karin VerspoorSummary
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.
Methods: We searched academic databases using artificial intelligence-assisted tools to capture all relevant literature from 2022 onwards.
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.
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.