(Cambridge, UK & Boston, USA - 10 March 2016) Linguamatics has been named in KMWorld’s list of “100 Companies That Matter in Knowledge Management” for the third year running. Now in its 16th year, the KMWorld 100 Companies That Matter list is compiled by KM practitioners, theorists, analysts, vendors and their customers and colleagues.

“It is an honor to be recognized once again by KMWorld as one of the 100 companies that matter in knowledge management,” comments John M. Brimacombe, Executive Chairman, Linguamatics.

“For the past 15 years, Linguamatics has pioneered innovative text mining technology to the life sciences, healthcare and related industries. The continued innovation in the last year has ensured Linguamatics has once again made this prestigious list of leading technology vendors.”

Since this time last year, Linguamatics became the industry’s first and only federated text mining provider. I2E’s Connected Data Technology allows users to run a single NLP query simultaneously over multiple data sources whether they are located locally, on Linguamatics’ cloud-based I2E OnDemand platform, or on third party servers elsewhere in the cloud. In 2015, the company saw a 30% increase in its total number of customers and 100% growth in its  healthcare business, where Linguamatics presence is becoming increasingly important.


Much of the work of researchers builds on previous discoveries, possibly best expressed by Isaac Newton: "If I have seen further, it is by standing on the shoulders of giants". In fact, one definition of research is: "a systematic investigation of sources in order to establish facts and reach new conclusions". To some extent, then, text analytics is a key tool for research, to enable users to see further and to reach new conclusions, by gaining a comprehensive and systematic view of what has already been found.

Clinical research is surely an area where re-use of data is of great scientific value. Using existing data to see further can bring benefits in speeding up drug development, and thereby enhancing patient care. Linguamatics have many customers using I2E to extract existing information from past and on-going clinical trials.

One example of data re-use is shown by Eric Su, Principal Research Scientist at Eli Lilly and Company. Eric uses I2E to extract summary statistics on clinical endpoints for therapeutic areas such as oncology and diabetes, to feed into clinical trial design and competitive environment analysis.


Natural Language Process (NLP) is a powerful tool for uncovering hidden secrets within unstructured text to analyze trends and reveal insights.

In healthcare, 60% of the 1.2 billion clinical documents produced in the US each year reside in unstructured narrative documents that would be largely inaccessible for data mining and quality measurement without NLP tools.

With NLP technology, organizations can unlock rich data to analyze patient populations and ultimately improve patient care.

In recent years, the use of NLP in healthcare has primarily been limited to disease-coding and research applications; however, Linguamatics was interested in discovering new opportunities that leverage NLP to enhance patient care and improve hospital efficiency.
 

Surveying healthcare system CMIOs

To that end, Linguamatics, with the support of the American Medical Informatics Association (AMIA), surveyed healthcare system CMIOs and asked them to share their visions for ways to leverage NLP to enhance patient care and improve hospital efficiency.

The participating CMIOs expressed overwhelming support for using NLP to help preserve the patient narrative and provide the insights required to meet accountable care objectives, including care delivery goals and the pro-active identification of high-risk patients.

They also voiced interest in leveraging NLP for a variety of other applications, including:


February 4, 2016 was World Cancer Day, and February is National Cancer Prevention Month. Throughout this month, individuals and groups worldwide are writing and sharing about the importance of taking steps to reduce your risk of cancer on an individual level and also the importance of cancer research on a clinical level.

Linguamatics are one of the pioneers in investing in Natural Language Processing (NLP) text mining technology to improve patient outcomes and cancer care, and one of the few companies using NLP at all. We have been working in healthcare for over 10 years, and recently announced a collaboration with Cancer Research UK to improve the characterization of cancer patient data for precision medicine.

NLP is growing rapidly in healthcare not only for research, but also now in widespread use for computer aided coding and computer aided document improvement. Simon Beaulah, our Director of Healthcare Strategy,  has published a white paper on 9 ways Natural Language Processing is being used by scientists to improve our (actionable) understanding of cancer. This highlights how, by applying NLP, significant impact can be achieved in improving cancer care by targeting the following areas:


Faster, better, cheaper... how often have we heard these words, in the context of any process along the long path of drug development? There are a myriad of solutions that can help at different stages, enabling more comprehensive target assessment, more rapid lead optimization, and so on.  One of the most expensive parts of the drug development process is clinical trials, with bottlenecks including access to knowledge for site selection, patient populations, principal investigators and key opinion leaders. 

Researchers naturally look to utilize information from current and past trials but manually extracting the relevant information can be resource-intensive, repetitive and, therefore, prone to errors.  Time is money, so reducing costs and errors is critical.  

One of our customers, Merck, use Linguamatics I2E for text analytics over public domain clinical trial data, to improve clinical trial site selection. 

One example of the benefits of text analytics is a site selection project for Merck Experimental Medicine division (EMS). They needed to locate a clinical trial site that would be able to conduct gastric bypass trials with the ability to measure gut peptides before and after surgery. The ideal trial site needed to fit many different characteristics - over a dozen - which would be hugely time-consuming to find using the public domain search interface to ClinicalTrials.gov.