The statistics of rare disease are challenging: there are over 7,000 rare diseases, and around one in 17 people will develop a rare disease. This represents a huge unmet need, as only about 5% of the known rare diseases have a licensed treatment.
To assist with drug development for rare diseases, it’s critical to capture the valuable information buried in unstructured text sources, such as full text literature and medical notes.
Join CCC and Linguamatics on Thursday, November 5 for half hour webinar where we will discuss the value of natural language processing to create the landscape of information around the natural history of disease, pathophysiology, and genotype-phenotype associations; and illustrate with use.
Register here.
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Capturing key information from a variety of sources and synthesizing into one platform can speed answers to key questions to confront the COVID-19 pandemic. Read more: How Natural Language Processing (NLP) Can Help Us Understand the Landscape of COVID-19 Information
Here is a look at two pharmaceutical use cases where text mining has transformed real world data into real world evidence: 2 Real World Examples: Using Real World Data for Commercial Pharmaceutical Product Insights