State-of-the-Art Healthcare NLP in 1 Line of Code

Transfer learning and deep learning unblocked new levels of accuracy for many medical natural language processing tasks. The session shares the current state-of-the-art accuracy on the most widely used healthcare NLP tasks: clinical & biomedical named entity recognition, relation extraction, assertion status (negation) detection, entity resolution (terminologies mapping), and de-identification. You'll see examples of how Spark NLP enables delivering this level of accuracy in real-world, production systems - adding privacy, tuning, scalability, and the ease of use of getting it all done with a single line of Python code.