Linguamatics enhances automatic text analysis

Linguamatics, a  developer of natural language processing (NLP) text analytics technology, has announced the latest release of its I2E AMP platform to automate the discovery of critical insights from text using NLP.

The I2E Asynchronous Messaging Pipeline (AMP) platform fault tolerant workflow management for real-time document and record processing, addressing the NLP text-mining and ETL (extract transform load) requirements for healthcare and life science organistions of all sizes by allowing users to plug I2E into enterprise workflows and rapidly process streams of data at scale.

I2E AMP 2.0 includes enhanced functionality to speed overall throughput and performance, sophisticated pre- and post-processing capabilities, a Web GUI to simplify the set-up of initial workflows, and new AMP Agents for smarter load balancing, easier deployment, and optimised I2E management.

"I2E's flexible NLP platform goes far beyond traditional entity mark-up, providing semantically enriched data that normalises concepts and relationships based on the relevant context," said David Milward , chief technology officer for Linguamatics.

"With AMP, clients now have an enterprise class, high-throughput solution that provides secure, fault-tolerant, scalable, and real-time ETL from unstructured text to structured data."

"AMP provides for the rapid transactional processing of unstructured data for a variety of workflows, including document markup and data extraction to feed enterprise search engines, machine learning, data warehouses, and dashboards," said Phil Hastings , chief business development officer for Linguamatics.

"The 2.0 release includes new options that make it an even more valuable solution that can be applied to multiple use cases across life sciences and healthcare."

Within the life sciences, I2E AMP enables the automated transformation of text into structured data across the drug discovery and development pipeline. I2E AMP is highly configurable, allowing the flexible use of a wide range of ontologies and business rules to be applied. Pharmaceutical companies are using I2E AMP in target selection, for example to generate target-indication dashboards from patents; in business intelligence to generate email alerts for clinical trial insights; to build safety databases from internal reports; and in regulatory affairs for document QA.

AMP allows healthcare users to quickly extract critical clinical details buried as unstructured text within EHRs, transform the text into discrete data, populate data warehouses, and support predictive risk models. New pre- and post-processing features enable the identification of document sections and evaluation of I2E NLP results for specific clinical criteria.

This realtime NLP capability supports a more complete, 360-view of each patient so that providers can easily detect patient risk and predict adverse events, match patients with clinical trials, or support clinical document improvement workflows.