Leveraging Foundation Models for Clinical Text Analysis
This work addresses information extraction from clinical texts for infectious disease research, but it is incremental as it applies existing transformer methods to a specific domain.
The study tackled the challenge of extracting key information from free-text clinical data for infectious diseases by proposing an NLP framework using a fine-tuned pre-trained transformer model, and it outperformed standard methods in evaluation.
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.