Many critical facts required by healthcare AI applications like patient risk prediction, cohort selection, and clinical decision support are locked in unstructured free-text data. Recent advances in deep learning have raised the bar on achievable accuracy for tasks like biomedical named entity recognition, assertion status detection, entity resolution, de-identification, and others.
This case study presents the first industrial-grade implementation of these new results and their application at scale.
Roche is the world’s #1 company for in-vitro diagnostics and its medicines are used to treat over 130 million people each year. It’s building a clinical decision support product portfolio, starting with oncology. Roche is using Spark NLP for Healthcare to extract clinical facts from pathology and radiology reports.
The case study covers the design of the deep learning pipelines used to simplify training, optimization, and inference of such domain-specific models at scale.