databricks-industry-solutions/adverse-drug-events
To ensure ongoing drug safety, pharma companies need to monitor and report adverse drug events post-market launch. This accelerator extracts, processes and analyzes adverse drug events from real-world text data using NLP
This project helps pharmaceutical companies and drug safety teams automatically identify and analyze potential adverse drug events (ADEs) mentioned in various unstructured text sources like emails, social media, and sales conversations. It takes conversational text as input and outputs structured information about drugs, ADEs, their relationships, and common patterns. Regulatory affairs specialists, pharmacovigilance analysts, and medical safety officers would find this tool useful for post-market drug surveillance.
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Use this if you need to systematically monitor vast amounts of informal text data to discover new or rare adverse drug events and understand their context.
Not ideal if your primary focus is on formal clinical trial data or structured adverse event reporting databases, as this tool is designed for unstructured conversational text.
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Language
Python
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Last pushed
Mar 02, 2023
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curl "https://pt-edge.onrender.com/api/v1/quality/nlp/databricks-industry-solutions/adverse-drug-events"
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