Life Science — Regulatory Document Knowledge Extraction Pipeline

Overview

The purpose of this DAG is to extract critical information from regulatory documents in the life sciences sector, utilizing Named Entity Recognition (NER) and classification techniques. The data sources include regulatory submissions, compliance documents, and clinical trial reports. The ingestion pipeline begins with the collection of these documents, followed by preprocessing steps that prepare the text for analysis. Processing steps involve applying NER to identify relevant entities and class

The purpose of this DAG is to extract critical information from regulatory documents in the life sciences sector, utilizing Named Entity Recognition (NER) and classification techniques. The data sources include regulatory submissions, compliance documents, and clinical trial reports. The ingestion pipeline begins with the collection of these documents, followed by preprocessing steps that prepare the text for analysis. Processing steps involve applying NER to identify relevant entities and classifying the extracted information into predefined categories. The results are then stored in a knowledge graph, which facilitates efficient information retrieval and enhances the ability to personalize customer interactions. Quality control measures are implemented throughout the process, including accuracy checks and regular audits to ensure the reliability of the extracted data. Key performance indicators (KPIs) for this DAG include the successful extraction rate and processing time, which are monitored to assess performance. In the event of failures, notifications are sent to responsible stakeholders to address issues promptly. The business value of this DAG lies in its ability to streamline regulatory compliance processes, improve data accessibility, and ultimately enhance customer engagement and satisfaction in the life sciences industry.

Part of the Customer Personalization solution for the Life Science industry.

Use cases

• Improves regulatory compliance efficiency and accuracy
• Enhances customer personalization through targeted insights
• Facilitates faster decision-making with structured data
• Reduces manual effort in data extraction and processing
• Supports ongoing regulatory updates and adaptations