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Modular Approach to Solve Problems at Scale in Healthcare NLP

Despite ongoing efforts towards using natural language processing (NLP) in information extraction from electronic health records (EHR’s), current solutions require healthcare AI practitioners to make unacceptable trade-offs between delivering state-of-the-art accuracy, generalizing over unseen data points, and preventing the sharing of personal data or intellectual property.

Spark NLP for Healthcare aims to bridge this gap by providing an accurate, scalable, private, and tunable software library that helps healthcare & pharma organizations build longitudinal patient records and knowledge graphs on real-world EHR data.

In this talk, Veysel presents the library’s latest advancements for making new state-of-the-art research work in practice across the industry:

1. What are the best practices for building production-grade solutions around the latest research?

2. When should you use lightweight language models over memory-intensive transformers?

3. How do you make models understand clinical context, by leveraging weighted sentence transformers while resolving clinical entities into medical terminologies?

4. How can data scientists who aren’t Healthcare NLP experts reuse dozens of industry-specific named entity recognition models & embeddings in one line of code?

Veysel will also share the latest benchmarks and state-of-the-art results that have been shipped within the production-grade library.

NLP with RPA

NLP is a disruptive technology in the world of ML and it’s important to take this to industrialize the same with real...