Unstructured free-text medical notes are the only source for many critical facts in healthcare.
As a result, accurate NLP is a critical component of many healthcare AI applications like clinical decision support, clinical pathway recommendation, cohort selection, patient risk, or abnormality detection.
Recent advances in deep learning for NLP have enabled a new level of accuracy and scalability for clinical language understanding, making a broad set of applications possible for the first time.
This NLP case study shows how Roche uses Spark NLP for Healthcare to extract & normalize tumor characteristics from free-text pathology and radiology reports.
In Oncology, thousands of pages of text can be accumulated for each patient, often over several years. Reading diverse reports and putting the facts together in a timeline is at the core of any real-world data collection effort.
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We help 5 of the top 10 global pharmaceutical companies make RWD and RWE a reality