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State-of-the-Art Healthcare NLP in 1 Line of Code – Ai4 Healthcare Summit 2022

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.

Connecting the dots in clinical document understanding with Relation Extraction at scale

Easy to use, scalable NLP framework that can leverage Spark. Introduction of BERT based Relation Extraction models. State-of-the-art performance on Named Entity...