With new and exciting technologies, it often happens that one particular application or use case leads the way initially… and then, when the euphoria turns into commercial reality, people start looking at other applications where the new technology can also bring value. In text mining, the same holds true. Pharma companies have now been using NLP text mining technologies for many years, in areas such as target validation, gene-disease associations, clinical trial optimization, and patent analytics, for example. As they become comfortable and, indeed, expert in these areas, attention has turned to areas where the core technology needs to be adapted or tweaked to meet a specific requirement.

For example, when looking to apply NLP to the time-consuming and costly business of discovering new, novel compounds, users hit a significant issue; trying to understand every single component part of some of the long chemical names. Not an insurmountable problem, but one that needed time, expertise and determination.


Linguamatics hosted our Spring Text Mining Conference in Cambridge last week (#LMSpring16). Attendees from the pharmaceutical industry, biotech, healthcare, personal consumer care, crop science, academia, and partner vendor companies came together for hands-on workshops, round table discussions, and of course, some excellent presentations and talks. 

The talks kicked off with a presentation by Thierry Breyette, Novo Nordisk, who described three different projects where text mining provided signficant value from real world data.  Thierry took the RAND Corporation definition: "Real-world data (RWD) is an umbrella term for different types of data that are not collected in conventional randomised controlled trials. RWD comes from various sources and includes patient data, data from clinicians, hospital data, data from payers and social data."

At Novo Nordisk they have gained business impact by text mining a variety of souces, including: social media to find digital opinion leaders; conversation transcripts between medical liaisons and healthcare professionals for trends around clinical insights; and mining patient & caregiver ethnographic data to see patterns in patient sentiment and compliance.


The Linguamatics Booth #345 at this year’s Bio-IT Conference (April 5-7 in Boston) offers the ideal opportunity to catch up with the latest developments in text mining.

Here are 3 reasons to meet the market leader in text analytics for life science and healthcare:


Guy Singh, Linguamatics Senior Manager, Product and Strategic Alliances, explains the key differences between keyword search and text mining.

See the full 52 second video below.

 

To learn more about how text mining works, check out our other video resources:

 


It was recently announced that Linguamatics has been named in KMWorld’s list of “100 Companies That Matter in Knowledge Management” for the third year running.

We are honored and recognize that making this list year-to-year isn’t a given.

3 key reasons why Linguamatics still matters in knowledge management in 2016:
 

  1. Leading NLP and text mining technology

Compared to other NLP text mining providers, I2E stands out for its ability to answer a wide range of questions, from simple open queries to questions that need advanced linguistic analytics.

Since this time last year, Linguamatics has become the industry’s first and only federated text mining provider.

Instead of having to run many text mining queries separately across disparate data sources, I2E’s Connected Data Technology allows users to run a single 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.