Melax Tech Collaborates with the University of Florida (UF) to Bring Enterprise-level Text Annotation Tool to Data Curation Team

Melax Tech, a world leader in biomedical Natural Language Processing (NLP) Technology, announced a continued collaboration with the University of Florida (UF) in Gainesville, one of the nation’s top five public universities and a leader in AI research and development. This alliance will provide UF researchers with the ability to unlock meaningful clinical information from electronic health record (EHR) notes.

LANN is a team-based text annotation tool that the UF data curation team will use to annotate clinical notes and train NLP models.

“We are excited to provide the University of Florida with an enterprise-level text annotation tool to help facilitate their NLP research focused on improving healthcare quality. I am looking forward to working with the UF data curation team on more NLP use cases in the future,” says Jingqi Wang, Chief Technology Officer at Melax Tech.

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According to Yonghui Wu, Ph.D., director of natural language processing at the UF-FSU Clinical and Translational Science Institute, Melax Tech’s LANN tool accelerated corpus annotation speed and efficiency in developing cutting-edge clinical natural language processing models.

“Creating high-quality corpora is a critical stage in developing natural language processing models to accelerate research and medical decision-making leveraging clinical narratives. With the help of LANN, we hope to efficiently organize multiple annotation projects, monitor annotation progress, and assess annotation quality.”

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Wu added that UF’s continued collaborations with Melax Tech could lead to innovative natural language processing tools and advanced, data-driven health research methods that were unimaginable even 10 or 15 years ago. For example, the GatorTron^™ model, which was released at UF in 2021 as the largest transformer model developed using more than 82 billion words of clinical text, can be applied to many critical NLP tasks through fine-tuning over good quality clinical corpora.

The benefits of this continued collaboration include the following:

• Accelerate corpus annotation speed and efficiency of the UF data curation team.
• Validate our NLP technologies on a large scale of EHR datasets.
• Build relationships with UF researchers and find potential collaboration opportunities.

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