Case Study:
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.
Why John Snow Labs?
Accuracy

Peer-Reviewed state-of-the-art accuracy
Keeps Improving with novel research for 4 years straight

Healthcare Expertise

250+ Pre-Trained Healthcare Specific NLP Models
Medical doctors and pharmacists involved in every project

Extensible

Train and Tune your own models on your own data
Support every medical document, including scanned images

Proven Success
We help 5 of the top 10 global pharmaceutical companies make RWD and RWE a reality