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:


Tom Schmidt, Managing Editor, IDG Strategic Marketing Services interviewed Dr. Jane Reed, Head of Life Science Strategy, Linguamatics, on how pharma and biotech companies use text analytics to reduce the time and cost of their clinical trials and get drugs to market faster.

The common statistic is that over 80% of data lies in unstructured text. Often, the way that people write things, whether in patents, healthcare records, or scientific literature, it's not easy to pull out the nuggets that are going to help with those decisions, whether around the real world value of your product, regulatory compliance, or many other different areas. Text analytics has to play a part in addressing many problems because of the volume of data that is unstructured.

Watch the full interview below.

With the ongoing focus on healthcare outcomes-based payment models, pharmaceutical companies face powerful pressures to demonstrate not just safety and efficacy of a new treatment, but also both cost effectiveness and comparative effectiveness. This means they must show that their agent is not only better than placebo but also better than other agents. Comparative effectiveness of any particular treatment can be established by interventional clinical trials, observational real-world evidence studies, or systematic review and meta-analysis. Access to on-going and past clinical trials via trial registries provides much valuable information, but effective search can be hindered by issues such as search vocabularies and problems of searching the unstructured text.

Merck recently published a paper, demonstrating the success of a text-mining pipeline that overcomes these issues and extracts key information for comparative effectiveness research from clinical trial registries. Researchers in the Informatics IT group wanted to search clinical trial registries (NIH, WHO International Clinical Trials Registry Platform (ICTRP), and Citeline Trialtrove) and synthesize comparative effectiveness data for a set of Merck drugs, in order to:

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.

I spent an informative and enjoyable day at the Findacure Scientific Conference last week, on Rare Disease Day, 29th February 2016. One of the aims of the charity Findacure is to find new cures for rare diseases by repurposing of existing medicines, and Dr Rick Thompson gave an excellent introduction to the problem, with an example of cost of illness modelling for Congenital Hyperinsulinism (CHI). This brought up some of the key challenges for disease modelling and understanding of rare diseases that were repeated again and again across the day:

  • Limited background information e.g. epidemiology and clinical burden of the disease
  • Paucity of knowledge of natural history of disease, and understanding of the disease heterogeneity
  • Little or no data on economic burden of the disease

The talks were varied, ranging from the cost effectiveness of potential drug repurposing programmes, the promise of big data and the ‘omics revolution in identifying suitable candidates for rare diseases, to how collaborations between academia, patient bodies, the pharma industry and rare disease charities are progressing discoveries and developments in certain areas.