Explore the details of my software engineering internship developing automated data pipelines and NLP solutions for pharmaceutical information processing.
At Zoomlogi, I am developing automated backend data processing pipelines that streamline the collection and organization of pharmaceutical information from FDA databases. The project involves applying NLP-based entity extraction techniques to consolidate diverse pharmaceutical data into a unified data platform.
This system enables efficient querying and analysis of pharmaceutical information, making it easier for healthcare professionals and researchers to access critical drug data. The automated pipelines reduce manual data entry efforts while improving data accuracy and consistency across the platform.
Working with FDA pharmaceutical data presented challenges in standardizing information from multiple sources with varying formats. I implemented robust NLP entity extraction models that could identify and extract key pharmaceutical entities regardless of format variations.
Another challenge was ensuring the automated pipelines could handle the large volume of pharmaceutical records while maintaining high accuracy. I designed validation systems and error-handling mechanisms to catch and flag potential data quality issues before they entered the unified database.
The automated data processing pipelines successfully consolidate pharmaceutical information from multiple FDA sources into a unified platform. The NLP-based entity extraction system accurately identifies and categorizes pharmaceutical entities, significantly reducing manual data processing time. The system is currently in active development and continues to improve through iterative refinements.