State-of-the-Art Clinical and Biomedical Natural Language Processing
, John Snow Labs
Many critical facts required by health-care AI applications, such as patient risk prediction, cohort selection, and clinical decision support, are locked in unstructured free-text data. Recent advances in deep learning and transfer learning have raised the bar on achievable accuracy for tasks like named entity recognition, assertion status detection, entity linking, de-identification, and relation extraction, using novel health-care-specific networks and models. We'll introduce Spark NLP for Healthcare: the world’s most widely used health-care NLP library, which holds a majority of top spots on accuracy leader boards, is optimized to run on modern GPUs, and is natively able to scale to run training and inference on Spark clusters. We'll use live and freely available Python notebooks to demonstrate how to achieve state-of-the-art accuracy and speed on common medical NLP tasks.