Webinar: Efficiency in Clinical Research - Artificial Intelligence - Natural Language Processing (AI-NLP)

The traditional ways of doing research isn’t sufficient to keep up with the need of modern medicine in the digital age. The vast amount of data needed to make strides in research is found in both the structured and the unstructured form. However, the majority of the data that is needed for improved outcomes is unstructured and it remains trapped within clinical records. Previously it was necessary for vast amounts of manual labor to be performed to unlock this knowledge. In order for research to be successful, many factors need to be taken into consideration including time and money. Often forgotten is the hidden cost of manual abstraction of patient charts. It is both costly and inefficient to fit the needs of individual research projects least not the institution as a whole. In this webinar we will discuss use cases where Artificial Intelligence - Natural Language Processing (AI-NLP) has made an impact on the success of research projects- from adding vital information to clinical registries, speedy identification of research cohorts to feeding unstructured data into Machine Learning.

Watch the webinar to learn how AI-NLP impacted:

  • Phenotype extraction (i.e. suspected genetic disorders)
  • Adding unstructured elements and associated attributes to clinical registries (i.e. cancer)
  • Identifying patients for clinical and observational studies
  • Mine scientific literature for insights into genetic mutation and disease association
  • Identify Social Determinants of Health (SDoH)
  • Adding unstructured data to Machine Learning (ML) models 

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