Researchers from Emory University and Georgia Institute of Technology have developed CLINGEN, a generic framework for generating high-quality clinical texts in few-shot situations. By combining clinical knowledge extraction from knowledge graphs and large language models, CLINGEN improves the variety and distribution of synthetic clinical data. Experimental results show consistent performance increases across multiple tasks.
Can Synthetic Clinical Text Generation Revolutionize Clinical NLP Tasks? Meet ClinGen: An AI Model that Involves Clinical Knowledge Extraction and Context-Informed LLM Prompting
Medical data extraction, analysis, and interpretation from unstructured clinical literature can be challenging. However, recent developments in large language models (LLMs) offer promising solutions. These models are pre-trained on large corpora and capture substantial clinical information.
Using LLMs directly for clinical text data analysis has limitations such as high infrastructure costs and privacy concerns. To address these issues, synthetic training data can be created using LLMs in a resource- and privacy-conscious way. Models trained on this synthetic data can achieve high performance while adhering to data privacy laws.
Researchers have developed CLINGEN, a generic framework that combines clinical expertise and LLMs to produce high-quality clinical texts in few-shot situations. CLINGEN uses clinical knowledge extraction to tailor prompts for specific clinical NLP tasks, resulting in improved variety and alignment with the original data distribution.
Key Contributions of CLINGEN:
- CLINGEN is a generic framework for creating clinical text data in few-shot circumstances.
- CLINGEN offers a method to use clinical knowledge extraction to tailor prompts for various clinical NLP tasks.
- Experimental results show that CLINGEN increases the variety of training samples and improves empirical performance across multiple tasks.
If you’re interested in evolving your company with AI, consider the potential of synthetic clinical text generation in revolutionizing clinical NLP tasks. To explore AI solutions for your business, connect with us at hello@itinai.com. Stay updated on AI insights and news by following us on Telegram t.me/itinainews or Twitter @itinaicom.
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