In pharmacovigilance, safety case data comes from unstructured documents such as emails from healthcare professionals, or from structured forms. The structured forms may contain some unstructured (free text) fields, and sometimes unstructured fields are handwritten. Feeding this data to a database is typically the first step in case processing. If this process is manual, then it can be tedious and error prone.
Automation through artificial intelligence and machine learning (AI/ML) can significantly speed up the case-intake process by automatically extracting safety case attributes from the structured and unstructured fields in adverse event (AE) forms or other source documents. It can also help improve the quality of data entered by manual operators and reviewers through visual hints and automated consistency checks.
Download this whitepaper to learn about:
• Using AI/ML for faster and accurate case intake and processing
• The overall process of software development using AI/ML models
• Deep learning and NLP use cases in pharmacovigilance
• Data quality assurance for software using AI/ML