Exploring LLM Annotation for Adaptation of Clinical Information Extraction Models under Data-sharing Restrictions
Abstract: In-hospital text data contains valuable clinical information, yet deploying fine-tuned small language models (SLMs) for information extraction remains challenging due to differences in formatting and vocabulary across institutions.
Since access to the original in-hospital data (source domain) is often restricted, annotated data from the target hospital (target domain) is crucial for domain adaptation.
However, clinical annotation is notoriously expensive and time-consuming, as it demands clinical and linguistic expertise.
To address this issue, we leverage large language models (LLMs) to annotate the target domain data for the adaptation.
We conduct experiments on four clinical information extraction tasks, including eight target domain data.
Experimental results show that LLM-annotated data consistently enhances SLM performance and, with a larger number of annotated data, outperforms manual annotation in three out of four tasks.
Paper Type: Long
Research Area: NLP Applications
Research Area Keywords: healthcare applications, clinical NLP
Contribution Types: Approaches to low-resource settings
Languages Studied: English
Submission Number: 3299
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