  Infectious diseases are a significant public health concern globally, and
extracting relevant information from scientific literature can facilitate the
development of effective prevention and treatment strategies. However, the
large amount of clinical data available presents a challenge for information
extraction. To address this challenge, this study proposes a natural language
processing (NLP) framework that uses a pre-trained transformer model fine-tuned
on task-specific data to extract key information related to infectious diseases
from free-text clinical data. The proposed framework includes three components:
a data layer for preparing datasets from clinical texts, a foundation model
layer for entity extraction, and an assessment layer for performance analysis.
The results of the evaluation indicate that the proposed method outperforms
standard methods, and leveraging prior knowledge through the pre-trained
transformer model makes it useful for investigating other infectious diseases
in the future.
