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Webinar: NLP in Precision Medicine: Real-World Clinical and Research Applications

When: Thursday, December 6, 2018
Time: 4:00 - 5:00pm GMT, 11:00 - 12:00pm EST


Precision Medicine has a tremendous potential to positively impact human health. AI techniques such as natural language processing (NLP) provide research and clinical teams with an exciting opportunity to realize that potential by effectively mining scientific and EHR data sources to better understand how genetics, environment and lifestyle factors can determine the best approach to prevent or treat disease with tailored approaches. This can be at the clinical level, or within drug discovery and development.

Within the clinical arena, in order to understand the best treatment pathway for a particular patient or group of patients, it is important to be able to access and analyse information about many different aspects of patients’ lives beyond just their medical history.

In the pharma industry, the annotation of high-throughput biological screens, such as next generation sequencing (NGS), can provide information for pharmacogenomic-related tailored drug development, from biomarker discovery and target evaluation, through to patient stratification and clinical profiling.

However, many of the sources needed for understanding detailed clinical phenotypes or genotype-phenotype relationships are unstructured text, which is not easily analyzed. I2E’s NLP-based text analytics unlocks the value from sources such as electronic health records (EHRs), scientific literature, conference abstracts, or internal reports.

In this webinar, you will learn how NLP is being used to transform unstructured source data into clinical and research decision support insights, and hear about some of the latest precision medicine application areas including:

  • Computational phenotyping with the Humana Phenotype Ontology
  • NLP-based assessment of variants of unknown significance in medical literature
  • Genotype-phenotype data mining for rare disease patient stratification

About the presenters:

Jane Z. Reed, Head of Life Science, Linguamatics

Jane Reed is Head of Life Science at Linguamatics. She is responsible for developing the strategic vision for Linguamatics’ growing product portfolio and business development in the life science domain. Jane has extensive experience in life science informatics. She worked for more than 15 years in vendor companies supplying data products, data integration and analysis, and consultancy to pharma and biotech—with roles at Instem, BioWisdom, Incyte, and Hexagen.

Simon Beaulah, Senior Director of Healthcare, Linguamatics

Simon Beaulah is responsible for Linguamatics’ healthcare products and solutions including applications in the areas of clinical risk models, population health, and medical research. 

Previously, Simon was Marketing Director, Translational Medicine at IDBS/InforSense where he was responsible for the company’s market analysis, product marketing and Go To Market strategy in healthcare analytics and translational medicine. Prior to IDBS, he was Director of Product Management at BioWisdom, where he was responsible for delivery of customer projects using the company’s ontology products. He also worked as a senior product manager at LION Bioscience and Synomics, and as a software developer at the UK’s Biotechnology and Biological Sciences Research Council. 

Simon has degrees from Aston University and Cranfield Institute of Technology.

Jeffrey Nauss, Senior Application Specialist

Jeff has worked with Linguamatics for over 10 years, supporting customers in their use of I2E, demonstrating the value of text mining, ensuring return on investment. Jeff has extensive experience with text mining and text analytics tools, training customers on the use of I2E, and working with customers on consulting and project work in both healthcare and pharmaceutical organisations. Previously, Jeff worked at Accelrys in product specialist and training roles; as Director of Molecular Modelling Services at University of Cincinnati; and has a PhD in Physical Chemistry from Rutgers University.

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