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We rely on obtaining as much information about a topic as possible to mitigate risk and unknowns in business and in our daily lives.  That knowledge becomes the backbone of our decision support. The trouble is, there is so much information produced daily, that in our hectic lives we can rarely go through it all, let alone sift through the information to only focus on pertinent knowledge to retain.  But are you finding the most up-to-date information? Is the information you are relying on for your decision support, years or even decades old?

Decision support straight to your inbox

Linguamatics NLP provides an Alerting capability to reduce the time required to review and provide results that are appropriate to your needs. Alerting allows you to schedule NLP search queries to be run at desired intervals, whether it is monthly, weekly, or even daily to keep up-to-date with your newest indexed information. 

This knowledge can be delivered via email to an individual or groups with the most recent and relevant information at your fingertips at all times. This broadens the benefits gained from an NLP approach – recipients of these emails can be across the organization, not just Linguamatics hands-on users.

The range and application of the alerting can be as broad as you need. You are not limited to one question but can schedule as many query alerts as you would like, to differing groups of recipients, as appropriate. This flexibility enables many different groups (e.g. different therapeutic area leads, medical affairs teams, safety assessment groups, to name but a few) to keep up-to-date.


Cambridge Healthtech Institute and Bio-IT World have awarded Roche a 2020 Innovative Practices Award in Focused Research for their use of the IQVIA Linguamatics Natural Language Processing (NLP) platform to glean patient insights from social media to improve clinical trial design.

Each year, Bio-IT World highlights outstanding examples of technology innovation in the life sciences. The Innovative Practices Awards are designed to recognize partnerships and projects pushing the industry forward, by sharing strategies that can be implemented across the industry to improve the quality, pace, and reach of the life sciences.

NLP over social media identifies clinical endpoints relevant to Parkinson’s disease patients

Bio-IT World judges selected Roche in the patient-focused research category for its work to discover if social media, particularly patient blogs and forums, can provide a good substrate to develop clinical endpoints relevant to Parkinson’s disease patients. Roche researchers established a series of NLP-based text mining queries to analyze patient discussions around Parkinson’s. The study identified symptoms confirmatory of the clinical trial endpoints, and also revealed new symptoms; a number of which have been added to the conceptual disease model used in the clinical trial.


Early Scientific Intelligence pipeline gives 360 degree view of “novelty” in diabetes and obesity

We hear a lot these days about evidence-based decision making. Particularly in the current climate, it’s critical that governments, businesses, health organisations and individuals are guided by facts and evidence, not fiction and hearsay. But what do we really mean by evidence-based decision making? Ideally, before making any decision, you want to be able to gather all relevant information, synthesized from different relevant sources. This approach allows you to see the overall picture, drill down to details, understand and weigh up the evidence and therefore make the best decision possible.

Creating a hub of evidence

Getting a comprehensive view of the whole picture is something Linguamatics pharma and healthcare customers need for their decision making in many different arenas - and that often means being able to integrate information from unstructured textual data streams together with data from structured sources. Capturing and integrating the information from a range of document sources can build a landscape of knowledge, a “hub” of evidence. Evidence hubs can be developed for discovery, development, regulatory affairs, safety, patient risk; with input data sources and output dashboards or alerts tailored as needed.

Novo Nordisk are using this integrated approach for an “Early Scientific Intelligence” evidence hub. Sten Christensen & Brian Schurmann (Novo Nordisk) presented on this innovative project at our virtual NLP summit on Thursday 4th June 2020.


In the rapidly evolving fight against COVID-19, IQVIA is committed to deploying our resources and capabilities to help everyone in healthcare do what needs to be done, and to keep things moving forward. Pharmaceutical and healthcare organizations, governments, and the broader scientific communities around the world are working to assess the impact of the virus, and how this can be tackled.

As part of this effort, it’s critical to have access to the best evidence from a broad range of data, including scientific literature, clinical trials and other textual sources. For intelligence from unstructured text, Linguamatics can help. Our Natural Language Processing (NLP) technology enables fast, systematic, and comprehensive insight generation from unstructured text. These sources can include scientific literature, clinical trial records, preprints, internal sources, social media, and news. Capturing key information from these many sources and synthesizing into one place – an Evidence Hub – gives users a deeper understanding of everything that’s going on. This approach can speed answers to key questions to confront the COVID-19 pandemic, such as:


Pharmaceutical companies are increasingly recognizing that patients’ social media posts are a valuable source of insight into patient-reported outcomes, views, symptoms, use of competitive products and more. Analyzing social media posts has the potential to provide insights from a broad population of patients, healthcare professionals, and key opinion leaders.

The standard methodology to gather these insights is primary market research. Pharma companies work hard to establish and maintain focus groups for live interviews, questionnaires and other ways to gather insights from patients and providers. However, the research process is time-consuming and expensive, requiring pharma companies to invest a significant amount of resources; and the number of patients that can be targeted is small.

Analysis of social media has the potential to be more efficient, more cost-effective, and address much larger patient populations than primary market research. The problem in analyzing social media content, however, is that social platforms represent a constantly flowing firehose of noisy information, so separating the signal from the noise poses a significant challenge.

Text mining with Natural Language Processing (NLP) technology represents an alternative approach to unlocking the value of social media content. By enabling artificial intelligence-based NLP tools to gather and analyze social data, pharmaceutical companies can more swiftly and efficiently glean insights that will influence the drug development process.