Life Science — Scientific Evidence Extraction for Regulatory Compliance

Overview

The primary purpose of the life_sciences_km1_evidence_extraction DAG is to extract and validate scientific evidence from both internal and external documents, ensuring compliance with regulatory standards in the life sciences sector. The data sources include internal databases, scientific publications, and regulatory documents, which are critical for maintaining compliance and supporting decision-making processes. The ingestion pipeline begins with data extraction from these diverse sources, fol

The primary purpose of the life_sciences_km1_evidence_extraction DAG is to extract and validate scientific evidence from both internal and external documents, ensuring compliance with regulatory standards in the life sciences sector. The data sources include internal databases, scientific publications, and regulatory documents, which are critical for maintaining compliance and supporting decision-making processes. The ingestion pipeline begins with data extraction from these diverse sources, followed by normalization to standardize formats and terminologies. This is followed by a classification step utilizing Named Entity Recognition (NER) techniques to identify relevant scientific evidence. Quality controls are implemented throughout the process, including validation checks to ensure the integrity and accuracy of the extracted information. The final outputs are stored in a centralized data warehouse, facilitating easy access and comprehensive traceability. Monitoring key performance indicators (KPIs) such as extraction accuracy, processing time, and compliance rates is essential for assessing the effectiveness of the DAG. The business value lies in streamlining the evidence extraction process, reducing manual effort, and enhancing compliance with regulatory requirements, ultimately supporting better decision-making and risk management in life sciences.

Part of the Supply/Demand Forecast solution for the Life Science industry.

Use cases

• Improved compliance with regulatory standards
• Enhanced accuracy in scientific evidence extraction
• Reduced manual workload for data processing
• Faster access to critical regulatory information
• Increased confidence in decision-making processes