Learning more about drugs understanding in the market

How can pharma product managers efficiently learn how their drugs are faring with patients in the market?

Product managers and teams in pharmaceutical companies need to know what patients and healthcare professionals are reporting and asking about their drugs as they are used in the market, in order to discern trends and patterns and respond appropriately. Real world data (RWD) on drug usage and patient behaviours is available in multiple formats from myriad sources, but mining these disparate structured and unstructured sources with traditional manual search and curation is time-consuming and inefficient.

Novo Nordisk wanted to accelerate, automate and scale this process to provide enhanced access to the extracted information for superior and actionable insights.

Natural Language Processing-based Text Mining at Novo Nordisk

Novo Nordisk was already using the Linguamatics NLP platform in-house on multiple individual text mining projects with good success (e.g. reducing a publication gap analysis from three-to-four people for six weeks to a few hours). They wanted to capitalise on this success for real world data about their diabetes therapeutic products, from medical affairs team, healthcare professionals, and patients.


Linguamatics NLP platform supports medical research and patient care delivery

Natural Language Processing (NLP) is used to transform text and unstructured data into valuable, real-life, outcomes. Generally in healthcare NLP is still in a relatively early stage of adoption. However, some organizations are moving forward towards full success in using NLP to deliver enhanced healthcare research and clinical processes.

Walter Niemczura, the director of application development at Drexel University College of Medicine in Philadelphia, is one of the individuals driving the ongoing initiative to improve healthcare research. Niemczura began working with Linguamatics seven years ago, in order to identify patients with certain characteristics that were well represented in unstructured clinical notes from Electronic Health Records (EHRs). Niemczura realized that the discrete data they had been working with wasn’t going to be enough to really advance and support research and patient care efforts.

"Linguamatics NLP was a huge time-saver. When you’re looking at hundreds of thousands or millions of patient records, the value might be not the ones you have to look at, but the ones you don’t have to look at." Walter Niemczura, director of application development, Drexel University College of Medicine


Huntsman Cancer Institute (HCI) at the University of Utah is a nationally recognized cancer center that relies heavily on data for its research studies. Because a vast amount of critical patient information is stored in unstructured formats such as clinical notes and pathology reports, finding specific data is often challenging, to say the least—not to mention costly and time consuming.

For years, HCI had compiled information manually or with rudimentary Natural Language Processing (NLP) tools, but surely there was a better way?

After encountering Linguamatics at an informatics conference and learning more about its NLP tools and Linguamatics text mining solution, the HCI research informatics team realized that this is what it had been searching for. To test the system, HCI used Linguamatics NLP platform in a project on breast cancer; it found that data capture was much, much faster, and using NLP improved access to higher quality data.

Since that initial success, HCI has expanded its use of the platform and developed NLP tools for multiple other conditions. It can now provide investigators with the quality data they need when applying for grants, writing papers, or identifying cohorts for specific studies. HCI has also been able to share data and collaborate with other institutions, to advance research and enhance disease understanding, and ultimately achieve better patient outcomes.


Linguamatics NLP platform enables rapid adverse event understanding from clinical trials

Identifying serious adverse events (SAEs) during clinical trials is a critical part of patient monitoring, and Agios wanted to enable a more rapid response to SAEs. These forms can be in image or PDF format, and manual extraction of the key patient data is slow and error-prone. Agios developed a workflow to process the Serious Adverse Event (SAE) report forms, using Linguamatics NLP platform to extract all relevant patient data. The workflow steps included:

  • OCR of the image SAE reports to render the data accessible
  • Indexing all documents with ontologies such as MeSH, MedDRA, WHO Drugs to normalize and code the data attributes
  • Using Linguamatics NLP platform queries to extract study drug, concomitant medications, adverse events, date of onset, lab test results and other key patient attributes 
  • Loading the data into a clinical safety database for rapid access

Identification of at-risk patients with network visualizations

A specific clinical example explored the risk of a rare (potentially life-threatening) adverse event, Differentiation Syndrome (DS) in patients on a clinical trial of Agios’s IDH1-inhibitor AG120. DS is a complication of first-line chemotherapy in some Acute promyelocytic leukemia (APL) patients, which can be fatal if not recognized on time and treated aggressively.


Notable successes include industry excellence awards, new product innovations, and strategic partnerships to extend application areas and solution benefits

Cambridge, England and Boston —Dec 12, 2018— In 2018, Linguamatics advanced its position as the market-leading provider of natural language processing (NLP) text analytics for healthcare and the life sciences, winning prestigious industry awards, introducing innovative product enhancements, and partnering with organizations to extend the value of Linguamatics solutions.

“Linguamatics has had an extraordinary year in terms of earning industry recognition, and advancing the use of our solutions and services across top-tier biomedical companies and health systems,” said Phil Hastings, Linguamatics chief business development officer. “At our Spring and Fall conferences we were inspired by our customers as they shared details on the many ways they are leveraging our AI solutions to extract actionable insights from unstructured text. We also heard new success stories from our latest industry partners who have selected Linguamatics to power automated solutions that unlock the rich knowledge assets within unstructured data.”

Some of Linguamatics’ most notable 2018 achievements include: