Life Science — Named Entity Recognition Extraction for Regulatory Research

Overview

The purpose of this DAG is to facilitate the extraction of named entities from a variety of scientific and regulatory documents, thereby supporting compliance research in the life sciences industry. It ingests data from multiple sources, including internal databases and PDF documents, which contain critical information for regulatory purposes. The ingestion pipeline begins with the preprocessing of text data to ensure uniformity and readiness for analysis. Following preprocessing, Named Entity R

The purpose of this DAG is to facilitate the extraction of named entities from a variety of scientific and regulatory documents, thereby supporting compliance research in the life sciences industry. It ingests data from multiple sources, including internal databases and PDF documents, which contain critical information for regulatory purposes. The ingestion pipeline begins with the preprocessing of text data to ensure uniformity and readiness for analysis. Following preprocessing, Named Entity Recognition (NER) models are applied to identify and extract relevant entities such as drug names, clinical trial identifiers, and regulatory references. Quality control measures are integrated into the workflow, including accuracy testing and compliance checks to ensure the reliability of the extracted data. The final results are stored in a centralized data warehouse, allowing for easy retrieval and further analysis. Key performance indicators (KPIs) such as extraction accuracy and processing time are monitored to assess the efficiency and effectiveness of the pipeline. This DAG adds significant business value by streamlining the literature review process, reducing manual effort, and ensuring that regulatory teams have access to accurate and timely information for decision-making.

Part of the Literature Review solution for the Life Science industry.

Use cases

• Enhances compliance research efficiency and accuracy
• Reduces manual data extraction efforts significantly
• Improves access to critical regulatory information
• Supports informed decision-making in life sciences
• Facilitates faster literature reviews and data analysis