Webinar: Improving patient recruitment and engagement with clinical trials using NLP

October 27, 2015

Only 3-4% of patients participate in clinical trials, limiting access to the latest treatments, impacting outcomes and resulting in significant lost data that could have been used to better understand the comparative effectiveness of treatments.

Linguamatics are using Natural Language Processing (NLP) to improve patient recruitment from multiple angles to ensure patients are effectively matched to trials and organizations can evaluate their patient populations for clinical trial eligibility. This webinar will demonstrate how patient data can be used to precisely filter potential trials from ClinicalTrials.gov and how NLP techniques can support cohort selection to assess populations for eligibility criteria using unstructured patient data.


Simon Beaulah, Director Healthcare Strategy, Linguamatics 
Sharon Zhang, Healthcare Application Specialist, Linguamatics 


35 minutes including Q&A


In this session we will discuss how to:

  • Enable your patients to be matched to a highly filtered set of clinical trials based on disease, location, cancer stage, ECOG, genetics and other medical and laboratory values from unstructured eligibility criteria
  • Interactively assess your patient populations for matches to clinical trials eligibility criteria using structured and unstructured patient data
  • Encourage patient engagement by providing a patient facing portal that promotes clinical trials specific to your academic medical center and also other trials in the local area

Who should attend?

Cancer centers:

  • Principle Investigators
  • Biobanking/clinical annotation
  • Bioinformatics
  • Population health analysts

Medical research:

  • Principle Investigators
  • Comparative effectiveness
  • Outcomes analysis
  • Predictive modelling

Pharma, Biotech and related industries interested in:

  • Real-world data access
  • Comparative effectiveness

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