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Join our seminar in San Francisco: Connected Natural language processing

Genentech, 475 E Grand Ave. Building 42, California, South San Francisco, 94080, United States


Time: 9:30am PDT, 12:30pm EDT, 5:30pm BST, 6:30pm CEST

Duration: 360 minutes

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

The agenda will include presentations from client organizations using IQVIA NLP, as well as product updates and overview from IQVIA. There will also be opportunities for knowledge sharing, hands-on learning, and time to network with IQVIA NLP experts, customers and other seminar attendees.  

Agenda Main Topics:  

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, or anyone struggling to get the information you need from volumes of unstructured records and texts – we can provide solutions and innovation
  • If you’re a data scientist, technical architect, executive in innovation, analytics quality or have an enterprise data role working with lots of unstructured data or looking for NLP solutions - this is for you.

Improving discreet cancer diagnoses from pathology reports with NLP: A case study in Cervical Intraepithelial Neoplasia - Soora Wi (The Permanente Medical Group)

The terminology used by pathologists to describe and grade dysplasia and premalignant changes of the cervical epithelium has evolved over time. Unfortunately, coexistence of different classification systems combined with non-standardized interpretive text has created multiple layers of interpretive ambiguity.  We used natural language processing (NLP) to automate and expedite translation of interpretive text to a single most severe, and thus actionable, cervical intraepithelial neoplasia (CIN) diagnosis.  The algorithms that were developed and then applied to 35,847 unstructured cervical pathology reports assessed NLP performance in identifying the severest diagnosis, compared to expert manual review. NLP performance was determined by calculating precision (0.957), recall (0.925), and F score (0.94).  Using NLP also significantly reduced the time to evaluate each monthly biopsy file from 30 hours to 0.5 hours. The use of NLP rapidly and efficiently assigned a discrete, actionable diagnosis using CIN classification that can assist with clinical management of cervical pathology and disease. Moreover, discrete diagnostic data encoded as CIN terminology can enhance the efficiency of clinical research. 

Genentech presentation

Michael Wu from Genentech will be presenting their work with NLP. 

Let’s get personalized – how IQVIA NLP drives precision medicine in Healthcare - Calum Yacoubian (IQVIA)

For true precision medicine, the importance of deep phenotyping is absolute. With increasing volumes of unstructured data vitally important to understand both the genetic basis of disease, but also the severity and progression of disease, repeatable means to create highly accurate curated features from these unstructured data is essential. In this session we will talk about how IQVIA NLP provides a repeatable and transparent means of accurate computational phenotyping that drives precision medicine. 

Spanning the gap: connecting data scientists to unstructured information - Paul Milligan (IQVIA)

Around 80% of a data scientists time is spent cleaning and preparing their data before it is ready to be processed. For unstructured information, this figure could be even higher when you factor in the lack of schemas, term ambiguity, inconsistent formatting and terminologies, etc. In the session, we will show how IQVIA NLP products can be used to obtain data from free text that has been cleaned, structured and normalized, allow users to spend their time on what matters: solving high-value business problems using the available tools and data. 

Improving risk adjustment and predictive analytics with AI augmented chart review - Paul Miligan (IQVIA)

Natural Language Processing is NLP is a key component of augmented intelligence in medical chart review, driving predictive analytics and risk stratification in healthcare. From government programs – such as Medicare Advantage Risk Adjustment – where chart review is necessary to ensure accurate capture and reporting of comorbid conditions, to predicting disease progression, there are many applications that are benefitted by bootstrapping chart review with AI. In this session – we will discuss how updates to the IQVIA NLP portfolio are helping healthcare organizations drive efficiencies in these areas.

Empower pharma decision-making with NLP from molecule to market - Jane Reed (IQVIA)

From bench to bedside, researchers and clinicians need to base decisions on the best possible view of data. As 80% of data resides in unstructured sources, rapid effective access to the knowledge buried in text documents is essential.  Natural Language processing (NLP) allows organizations to transform unstructured text into actionable structured data that can be rapidly visualized and analyzed, for decision support. We will present how NLP provides a powerful solution to these challenges, with customer use cases ranging from disease understanding to medical affairs.

Lost in data: empowering life science users to find the right information - Jane Reed (IQVIA)

Powerful text-mining technologies can prove invaluable for everyone from data scientists and technology teams through to scientists and professionals across pharma functions. In this session we will take a closer look at three scalable ways we can put the power of NLP into the hands of these teams in safety, regulatory and medical affairs. The case studies and examples in this session bring to life how text mining scientific literature, drug labels & regulatory documents, social media and news sources can unlock new insights or help teams work more efficiently.  By empowering users to search, compare, extract and analyse relevant rich data that is otherwise buried within text and document sources, teams can focus on applying their expertise and less on sifting through vast data sources for the information they need to get started.  


Calum Yacoubian - Director NLP Healthcare Strategy
Calum Yacoubian
Director NLP Healthcare Strategy
Paul Milligan - Director, Product Strategy
Paul Milligan
Director, Product Strategy
Peng Zhang - Senior Application Scientist
Peng Zhang
Senior Application Scientist
Jane Reed - Director, NLP, Safety & Regulatory
Jane Reed
Director, NLP, Safety & Regulatory
Michael Wu
Michael Wu
Sr Informatics Analyst at Development Sciences Informatics
Soora Wi - Consulting Manager, Permanente Medical
Soora Wi
Consulting Manager, Permanente Medical

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