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Drug safety is, understandably, a prime concern for pharma organizations, regulators, health authorities and patients alike. While there is always risk associated with any medication or treatment, the aim is always to understand any risk so that it can be handled or mitigated appropriately.

The holy grail is, then, how can we predict risk effectively? This is a huge focus of many research initiatives and is being address at many levels – drug target, molecule, patient, population. With the recent flourishing of AI/ML, we’ve seen a blossoming of models to enable risk prediction.

Pilot paper demonstrates use of NLP for adverse events to feed machine learning

There is ongoing work at the FDA to develop models that can predict adverse events (AEs) using post-market safety data, for new drugs coming on the market. Two papers published this year use a combination of AI/ML tools, including NLP, ensemble models and classification algorithms. Both papers build upon pilot work. The pilot study of six drugs demonstrated that pharmacological target AE profiles, based on marketed drugs, can be used to predict unlabelled adverse events for a new drug at the time of approval.


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.


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:


We are proud to announce that the IQVIA Linguamatics Natural Language Processing (NLP) platform was recently awarded Questex’s 2019 Fierce Innovation Award – Life Sciences Edition in the Data Analytics/Business Intelligence category. In addition, the NLP healthcare platform was recognized with a Best in Show honor for ‘Best Technology Innovation’.

Sponsored by the publisher of FierceBiotech and FiercePharma, the Fierce Innovation Awards identify and showcase outstanding innovation that is driving improvements and transforming the healthcare industry. An expert panel of judges reviews all submissions to determine which companies demonstrate innovative solutions, technologies and services that have the potential to make the greatest impact for biotech and pharma companies.

The IQVIA Linguamatics NLP platform supports life sciences organizations seeking to speed up drug development and improve patient outcomes by breaking down data silos, boosting innovation, enhancing quality and reducing risk and complexity. NLP is an artificial intelligence technology that transforms unstructured and semi-structured text into normalized, structured data suitable for analysis or to drive machine learning algorithms.

The platform uses sophisticated algorithms to identify, extract and connect key concepts, facts and relationships buried in the text rather than just retrieve documents based on keyword search. Key solution areas include: