Automated Detection of Invasive Fungal Infections in Clinical Reports Using Medical Language Models
- Author(s)
- Han, W; Martinez, D; Rozova, V; Cavedon, L; Khanina, A; Worth, LJ; Slavin, MA; Thursky, KA; Verspoor, K;
- Journal Title
- Studies in Health Technology and Informatics
- Publication Type
- Research article
- Abstract
- Invasive fungal infections (IFIs) pose significant risks to patients with weakened immune systems, requiring timely detection. To improve IFI detection from clinical reports, we explore the value of recent advances in NLP techniques for this task, including transformer-based pre-trained language models (PLMs) and generative large language models (LLMs). Experimental results show these methods are more effective for IFI detection than prior approaches, with a hybrid approach missing only one positive case over a public benchmark dataset, CHIFIR. These findings highlight the value of modern NLP methods, and the utility of combining diverse approaches.
- Publisher
- IOS Press
- Keywords
- *Natural Language Processing; Humans; *Invasive Fungal Infections/diagnosis; *Electronic Health Records; Automated Surveillance; Invasive Fungal Infections; Large Language Models; Natural Language Processing; Pre-trained Language Models
- Department(s)
- Infectious Diseases
- Publisher's Version
- https://doi.org/10.3233/shti250990
- Terms of Use/Rights Notice
- Refer to copyright notice on published article.
Creation Date: 2025-09-02 05:52:26
Last Modified: 2025-09-02 05:52:32