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2023 Seminar, Princeton: Connected Natural Language Processing

Crowne Plaza Princeton, 900 Scudders Mill Road, New Jersey, Plainsboro, 08536, United States


Time: 7:00am PDT, 10:00am EDT, 3:00pm BST, 4:00pm CEST

Duration: 280 minutes


Natural language processing is a powerful technology that is being applied in new and innovative ways across healthcare and life science.

Join our in-person seminar series to hear about the advances in NLP and machine learning techniques being applied within healthcare, pharma, and government organizations. Learn about new applications for leveraging NLP to unlock key insights from unstructured text to improve patient outcome, safety assessment, brand awareness, and more.

The seminar will focus on topics such as precision medicine, risk adjustment, population health as well as generating high-quality data and insights for drug discovery, safety, regulatory and medical affairs teams. 

During this one day seminar, there will be customers’ use case presentations using IQVIA NLP, product updates and IQVIA overview along with networking opportunities with your peers and IQVIA NLP Experts, knowledge sharing and hands-on learning.

Topics covered:  

The final agenda will be available in the weeks ahead but use cases will cover: 

  • Providers & Health Systems, Payers & Insurance Companies 
  • Pharma discovery, safety, regulatory, and medical affairs 

Who should attend:

  • Current customers – we would love to say hi in person and show you our latest solutions 
  • Researchers, scientists, product teams, clinicians
  • Data scientist, technical architect, executive in innovation, analytics quality or data role working with lots of unstructured data
  • Anyone struggling to get the information you need from volumes of unstructured record and texts

This event we will be a good opportunity to learn about Innovative NLP Solutions and how others use NLP to solve their data challenges.

Reducing the Noise: Innovative NLP in Healthcare by Ryan Hulscher, Product Manager, AI Solutions Delivery at IQVIA

Across drug discovery and development, scientists, clinicians, product teams and more need access to the information buried in unstructured text. AI technologies can reduce manual effort and provide effective access to the critical information for your decision support.  

In this presentation, we will provide an update of some of the latest innovations in AI. We will talk about the ability to bring machine learning models (such as BERT and GPT) into our technology platform, to allow for the combination of multiple NLP technologies. We will demonstrate IQVIA Human Assisted Review Tool, which enables effective document curation to speed up review and generate annotations for learning NLP. We will also showcase some recent use cases from customers, such as Genentech and AstraZeneca. 

Innovating with AI: digital transformation for safety and regulatory affairs by Jane Reed, Director NLP Safety & Regulatory at IQVIA

The pharmaceutical industry is one of the world’s most heavily regulated industries, and the traditional document-centric approach poses challenges in terms of efficiency, cost and value. The ever-increasing volume of safety records and relevant regulatory data mean companies need tools and solutions to assist with safety and regulatory processes. Many companies are looking to innovative new technologies to transform document-driven processes to data-driven ones. In this session, we will discuss the potential that innovative NLP can bring, for digital transformation, automation, and human-in-the-loop augmented intelligence, presenting use cases from the FDA and top pharma. 

Three ways NLP brings value to Medical Affairs, Real-World & Commercial teams by Hywel Evans, Director NLP for Medical Affairs & Commercialization at IQVIA

There is a huge amount of valuable text and document-based information available to medical and commercial teams, but it is often a challenge to work with. From notes captured by field teams that capture the unmet needs of physicians, patient perspective online, to the latest scientific literature and congress abstracts, this data can inform content development, medical and commercial strategy, competitive insights and more. Join us to hear about three examples of applied AI and NLP to unlock these invaluable data sources.

Using machine learning and AI to scale a clinical text anonymization pipeline by David Di Valentino, AI/ML Solutions Lead for Privacy Analytics at IQVIA

Unstructured text data from clinical and medical domains can generate substantial insights for healthcare organizations looking to drive innovation and improve outcomes for patients. In many cases, anonymization is the most practical way to leverage such data for secondary uses including research, analytics, and AI/ML.


However, anonymization of clinical text can be a difficult space to navigate, with numerous potential pitfalls to be aware of: Ensuring compliance with legal and regulatory requirements; dealing with dirty or non-standard data; and properly applying safe, privacy-protective techniques. 


While the use of machine learning (ML) brings its own inherent complexities, it also provides powerful tools that can bring scalability and increased efficiency to the anonymization process and help to navigate (or bypass) commonly encountered pitfalls.


In this talk, we will present case studies in which we successfully leveraged AI/ML technologies to scale up our unstructured text anonymization workflow to handle large and complex clinical texts. We will discuss the special considerations we made to handle this data in an ML context and enumerate the benefits we realized in terms of scalability and effort savings.

Accelerating Drug Development with NLP by Constantinos Kateatis, Associate Director NLP R&D at IQVIA

The growing document volume and varied data sources in drug development is a constant challenge. Natural language processing (NLP) technologies are key to efficiently keep up with the latest information. Generate insights 10-100x faster by combining new innovative large language models (LLMs) with established methods for a range of drug development tasks, including target identification, target prioritization, clinical trial design, and more. 

Anonymization of clinical documents for publication – inherent challenges and regulatory perspectives by Niamh McGuinness, Regulatory Segment Lead for Privacy Analytics at IQVIA

As part of a continued shift to greater transparency in clinical trials, sponsors must comply with increasingly demanding document publication requirements, such as those under EMA’s Clinical Data Publication Policy (Policy 0070), Health Canada’s Public Release of Clinical Information (PRCI) and more recently, the EU Clinical Trials Regulation. These initiatives require the sharing of anonymized versions of a broad range of document types, many of which contain dense participant data.

While rules-based redaction of this information is acceptable, even this simple solution can be difficult to achieve especially over tens of thousands of pages. Furthermore, regulators have been vocal and explicit in their preference for a robust, utility-preserving method such as statistical anonymization. This approach requires that participant data be transformed in such a way as to achieve defensible privacy protection. 

In this talk, we will outline some of the common challenges and pitfalls in handling statistical anonymization of clinical documents for public disclosure and set the scene for how AI/ML technologies can be successfully leveraged to scale up a workflow for even the largest and most complex clinical texts.


Jane Reed - Director, NLP, Safety & Regulatory
Jane Reed
Director, NLP, Safety & Regulatory
Hywel Evans, Director, NLP for Medical Affairs & Commercialization
Hywel Evans
Director, NLP for Medical Affairs & Commercialization
Constantinos Katevatis is Associate Director, R&D NLP at Linguamatics, an IQVIA company
Constantinos Katevatis
Associate Director, R&D NLP
David Di Valentinos
AI/ML Solutions Lead for Privacy Analytics
Niamh McGuinness
Regulatory Segment Lead and Transparency Advisor
Ryan Hulscher
Product Manager, AI Solutions Delivery at IQVIA
Kathleen Nogueira
US Delivery Team Lead at IQVIA
Marjan Kamyab
NLP AI Solutions Engineer at IQVIA

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