Demos
Case Study:
Automated and Explainable Deep Learning for Clinical Language Understanding at Roche
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?
01
Accuracy
Peer-Reviewed state-of-the-art accuracy
Keeps Improving with novel research for 4 years straight
02
Healthcare Expertise
250+ Pre-Trained Healthcare Specific NLP Models
Medical doctors and pharmacists involved in every project
03
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
Using Spark NLP to Enable Real-World Evidence (RWE) and Clinical Decision Support in Oncology
Applying State-of-the-art Natural Language Processing for Personalized Healthcare
