Scalable Incident Detection via Natural Language Processing and Probabilistic Language Models

This presentation described a scalable approach to identifying clinical events at the health system scale using natural language processing (NLP) and probabilistic language models. Methods were discussed and demonstrated in two challenging and clinically important use cases: suicide prevention and sleep-related behaviors. This approach has implications for post-market safety surveillance, for clinical prevention, population health, and more.