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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 Recognition and Relation Extraction. Reported SOTA performance of multiple public benchmark datasets. Application of these models on real-world use-cases.

We present a text mining framework based on top of the Spark NLP library — comprising of Named Entity Recognition (NER) and Relation Extraction (RE) models, which expands on previous work in three main ways. First, we release new RE model architectures that obtain state-of-the-art F1 scores on 5 out of 7 benchmark datasets. Second, we introduce a modular approach to train and stack multiple models in a single nlp pipeline in a production grade library with little coding. Third, we apply these models in practical applications including knowledge graph generation, prescription parsing, and robust ontology mapping.

AI In Health Care: Trends And Challenges In 2022

Around this time last year, the 2021 AI in Healthcare Survey was released. The results showed growth in natural language processing (NLP),...