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Automated Drug Adverse Event Detection from Unstructured Text

Adverse Drug Events (ADEs) are potentially very dangerous to patients and are amongst the top causes of morbidity and mortality. Monitoring & reporting of ADEs is required by pharma companies and healthcare providers. This session introduces new state-of-the-art deep learning models for automatically detecting if a free-text paragraph includes an ADE (document classification), as well as extracting the key terms of the event in structured form (named entity recognition). Using live Python notebooks and real examples from clinical and conversational text, we’ll show how to apply these models using the Spark NLP for Healthcare library.

Why Personal COVID-19 Vaccination Data Should Remain Private

Earlier this month, it was reported that the Centers for Disease Control and Prevention (CDC) instructed states to sign data use agreements that commit...