Drug safety is of critical importance, at all stages in drug discovery, development and delivery. Across the whole span, safety-relevant data is being both generated and sought from unstructured text – from internal safety reports, scientific literature, individual case safety reports, clinical investigator brochures, patient forums, social media, conference abstracts.
Intelligent search across these hundreds of thousands of pages can provide the information for key decision support. Life science companies are actively pursuing innovative technologies to address the challenge of managing increasing volumes of safety-related data from new and disparate sources. Many of our customers are using the power of Linguamatics Natural Language Processing (NLP) platform to transform the unstructured text into actionable structured data that can be rapidly visualized and analyzed, at every stage through the safety lifecycle of a drug.
This webinar presents an overview of customer success stories, to show best practice use of this Artificial Intelligence (AI) technology to advance drug safety.
- How natural language processing (NLP) text mining can extract structured data from unstructured text in scientific papers, clinical trial databases, internal safety reports, FDA drug labels, and more.
- How big pharma access internal data silos and external data sources for safety decision making. Use cases from top pharma and the FDA are discussed.