TL;DR , Spark NLP for Healthcare comes with 600+ pretrained clinical pipelines & models out of the box and is consistently making 4–6x less error than Azure, AWS and Google Cloud on extracting medical named entities from clinical notes. It comes with clinical and biomedical named entity recognition (NER), assertion status, relation extraction, entity resolution and de-identification modules that are all trainable. Spark NLP for Healthcare already has 100+ clinical named entity recognition (NER) models that can extract 400+ different entities from various taxonomies.

Spark NLP is a Natural Language Processing (NLP) library built on top of Apache Spark ML. It provides simple, performant & accurate NLP annotations for machine learning pipelines that can scale easily in a distributed environment. Spark NLP comes with 5000+ pretrained pipelines and models in more than 200+ languages. It supports nearly all the NLP tasks and modules that can be used seamlessly in a cluster. Downloaded more than 25 million times and experiencing 10x growth over the last year, Spark NLP is used by 41% of healthcare organisations as the world’s most widely used NLP library in the enterprise [1]. In order to learn more about the components of the Spark NLP ecosystem, please watch this recording, visit and also check out a quick deep dive session for free.