Automated Detection of Invasive Fungal Infections in Clinical Reports Using Medical Language Models

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.

Keywords: Automated Surveillance; Invasive Fungal Infections; Large Language Models; Natural Language Processing; Pre-trained Language Models.