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Press release: Linguamatics Announces University of Pennsylvania Health System as Latest Enterprise NLP Client

I2E Natural Language Processing advances research and care delivery by mining clinical insights from unstructured patient data

Cambridge, UK & Boston, USA – June 22nd, 2017 – Market leading Natural Language Processing (NLP) text analytics provider Linguamatics today announced the implementation of the Linguamatics Health enterprise NLP platform, powered by I2E, at the University of Pennsylvania Health System for the extraction of actionable insights from unstructured patient data.

“We look forward to working with Penn Medicine to help them unlock valuable insights from clinical notes in order to advance research initiatives and enhance the delivery of care,” said Simon Beaulah, senior director of healthcare at Linguamatics. “Our growing community of academic medical centers across the country have deployed the Linguamatics Health platform, and are taking advantage of its ease of use, powerful NLP capabilities, rapid query development and successful integration with enterprise systems. Our platform is particularly well-suited for this environment because it empowers organizations to work independently, and get the data they want without requiring extensive services.”

The University of Pennsylvania Health System will leverage the Linguamatics Health platform to build queries and automatically mine clinical data from patient encounter records, specialist reports and unstructured EHR notes. Specific use cases will include the identification of cohorts of patients with certain medical conditions and locating additional clinical annotations for the PennOmics system for translational research.

“Our organization needed an NLP tool to make unstructured clinical data more accessible for our research and clinical efforts,” said Jason Moore, director of the Penn Institute for Biomedical Informatics (IBI). “We look forward to exploring different opportunities to use Linguamatics I2E’s NLP capabilities to gain additional insights from our unstructured patient data.”

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