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Artificial Intelligence for Pharmacovigilance Processing – What is Possible Today

Drug side effect reporting (also called pharmacovigilance) is a massive undertaking for biotech and pharmaceutical companies as well as for Regulatory agencies. Lately, with global COVID-19 vaccine programs, further impact on side effect volumes have been experienced, making it a costly and time-consuming process. Using NLP, and other automation techniques, is a promising way to cope more efficiently with work-loads, but here a number of challenges are to be tackled. In this session, we will go through learnings from a previous drug safety-focused “AI Data Analyzer” public-private (Innobooster) initiative as well as the ongoing NLP implementation using Spark NLP with a major Regulatory agency, with the aim of suggesting how and where to get the most value of machine-derived data in the actual processing of side effect reports.

Deep Learning for Relation Extraction from Clinical Documents

Unstructured free-text medical notes are the only source for many critical facts in healthcare. As a result, accurate natural language processing (NLP)...