Four million people die from diabetes annually. Novo Nordisk, a global healthcare company, has a mission to change that. Although it has a presence in 170 countries, is already helping 28 million patients, and supplies half of the world’s insulin, the company still faces an enormous challenge: novel drug approaches are needed, and drug development is a long, expensive process. The GLIA (Global Information & Analysis) team at Novo Nordisk aim to help by providing the best information possible to researchers and product teams.
Using natural language processing (NLP) to extract information from real world data sources
The answers Novo Nordisk need are buried within a myriad of sources of unstructured real world data. These data sources include research papers, news reports, market information, patient use information, and more.
“Finding accurate information in an ever-growing ocean of information is becoming more important than ever,” explains Novo Nordisk senior information scientist Solmaz Gabery Adams.
Extensive research informs every step on the long path to delivering healthcare, from identifying needs and undertaking drug discovery to clinical trials and regulatory review before bringing new treatments to market. At every stage, Novo Nordisk researchers and managers must make crucial decisions, including which projects to advance and which projects to leave behind.
That means the right information at the right time can make all the difference for patients, potentially shaving months or even years from the drug development process.
Unfortunately, with so much of the potentially useful information that researchers, clinicians, and others needed to do their best work locked up in unstructured sources, company leaders often had to weigh the pros and cons of spending the time and resources required to manually sift through documents.
Novo Nordisk needed a solution that would quickly cut through the noise of unimportant information to find the gems, and also build a database that would continue to add value to future research efforts in the quest to find and help more patients. That’s where the power of NLP text mining came in.
Case study: using NLP for Dow Jones news feeds
As part of Dow Jones Developing your Data-Driven Business: A Thought Leadership series, we have published a joint case study with Novo Nordisk and Dow Jones entitled: Text Mining for Competitive Intelligence.
The GLIA team wanted to increase the value from Dow Jones news feeds. News gives the most up-to-date compared to literature, but news feeds are noisy, and manual review to find the key information is slow. Using Linguamatics I2E, they were about to bring a sense of structure to news data, automatically extract insights, and bring valuable and timely information to internal stakeholders such as product teams.
Download the whitepaper to find out more about how Novo Nordisk have built a successful data and technology ecosystem to uncover competitive insights using text mining and natural language processing of news data using Linguamatics technology and Dow Jones DNA.
Figure: Once the news feeds are structured and tagged with suitable topics, the information can be visualised in dashboards for rapid assimilation by product teams. This schematic shows frequency of news reports for the key Novo Nordisk products, and further drill-downs provide more details.
Learn more about Transforming Real World Data into Insights
