Interest in artificial intelligence (AI), particularly natural language processing (NLP) and machine learning, has grown significantly within the healthcare community in recent years, as vendors, researchers, and providers look for ways to transform medical research and care through technology.

How do these techniques work? Machine learning can help to solve complex issues by analyzing existing data from sources such as electronic health records (EHRs), but often the data contained within EHRs is “trapped” in unstructured medical notes. NLP can interpret structure and meaning in this unstructured text, and make critical information accessible to machine learning applications.

Read the full Health IT Outcomes article to find out more about how the combination of NLP and machine learning can deliver a powerful solution for advancements in the understanding and delivery of care.

Read the full article

About Simon Beaulah:

Simon Beaulah is Linguamatics’ senior director of healthcare and is responsible for the company’s healthcare products and solutions, including applications for clinical risk models, population health, and medical research.


NLP text mining transforms medical transcripts data into insights for better patient care

Santa Cruz, CA, Cambridge, UK & Boston, USA – August 3rd, 2017 – Natural Language Processing (NLP) text analytics provider Linguamatics, and RealHealthData, a narrative medical records database provider, today announced their strategic partnership to combine Linguamatics’ advanced NLP technology with RealHealthData’s extensive database of detailed provider narratives, to improve the understanding of drug use, adverse events, and product switching in Real World settings.

Understanding the real world (i.e., outside of clinical trials) impact of therapies on patients is critical for pharmaceutical and biotech companies. Medical records are one of the key sources of real world data, and provide evidence that can inform all phases of drug development. RealHealthData provides access to patient narratives from all 50 US states and every medical specialty. The data can be used for all phases of drug development and post marketing research. Linguamatics I2E can be used to extract the key facts from these narratives using relevant ontologies and queries, transforming real world data into actionable intelligence for better decision making.

“Deploying Linguamatics I2E Advanced NLP engine to the RealHealthData database of detailed provider narratives is a natural fit,” said Manuel Prado, CEO of RealHealthData. “Current and future customers can now access the unique and valuable insights in the database using a first-in-class, healthcare-specific Natural Language Processing platform.”


I2E makes natural language processing-based text mining intuitive and interactive

SANTA CLARA, Calif. — July 20, 2017 — Based on its recent analysis of the Big Data text analytics market for the healthcare industry, Frost & Sullivan recognizes Linguamatics with the 2017 Global Frost & Sullivan Award for Enabling Technology Leadership. Linguamatics stands out in the natural language processing (NLP) market for its technology expertise and commitment to delivering exceptional value to clients in the US healthcare industry. The highly flexible and scalable Linguamatics Health platform, powered by I2E, is helping healthcare providers and payers to transition to value-based care.

Within the last year, Linguamatics introduced its fifth iteration of I2E, which includes cutting-edge capabilities such as the normalization of concepts and relationships for quick and comprehensive data retrieval regardless of format; advanced range research; and an extraction and search query language (EASL). The EASL can be generated external to the platform to support custom interfaces, queries in a human-readable format, and superior workflow automation.


In healthcare, the excitement about the potential for big data and machine learning is palpable, and there is more accessible electronic information than ever before.

The challenge for the healthcare community is that approximately 80% of the data in a typical electronic health record (EHR) is trapped within unstructured notes, which requires expensive human annotation to make it accessible to machine learning systems.

So what’s the solution? The use of Natural language processing (NLP), another artificial intelligence (AI) technique, can turn this unstructured text into a set of features for machine learning to use. Data-driven, rule-based NLP techniques can extract information from text using linguistic patterns and terminologies with high precision and recall —avoiding the need to manually annotate training data for the machine learning model.

Read the full PM360 article to find out more about how the combination of NLP and machine learning can be a powerful tool for developing predictive models in healthcare and life science.

Read the full article

About David Milward:

David Milward is Chief Technology Officer at Linguamatics. He is a pioneer of interactive text mining, and a founder of Linguamatics. He has over 20 years of experience in natural language processing (NLP) product development, consultancy, and research.


With a background such as mine - medicine/ information technology/ government/ military - you need to know your audience, and ensure acronyms are appropriate.

In healthcare alone, DOA can mean several things: degenerative osteoarthritis, date of arrival, drug of abuse, dead on arrival, etc. Most of which I REALLY don’t want to see in a healthcare analytical report for Rheumatology.

Although ETL is no exception, it is widely used in the world of healthcare now as “Extract Transform and Load” and - unless you are speaking to a someone in the area of pulmonary and respiratory diseases - it will seldom get confused with “expiratory threshold load” which helps determine respiratory muscle efficiency. Then there is AMP, which in medicine is most commonly known as a adenosine monophosphate a vital component in all living cells. But for Linguamatics Health users, AMP is an acronym that is vital in it’s own right and stands for Asynchronous Messaging Pipeline.

Here at Linguamatics we are grateful to have some very talented folks that can explain our technological world in a way that is (sometimes) less technical. Alex Richard-Hoyling ( Senior Solutions Developer) explained how he helps ensure reliable data extraction in large healthcare systems via the Linguamatics Community. Below, I take the subject a step further to cross the chasm of where tech meets med.