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A leading global organization in the healthcare and pharmaceutical sector needed a scalable solution to classify thousands of unstructured product descriptions—spanning medical devices, pharmaceuticals, consumables, and supplements—into accurate, predefined categories. This classification was critical to support valuation processes, regulatory compliance, and streamlined operational workflows.
The client faced a major bottleneck in managing and categorizing large volumes of product data stored in spreadsheets. The descriptions were highly varied, unstandardized, and included a mix of technical, commercial, and medical terminology. Manually processing such a vast dataset was not only time-consuming but also prone to errors and inconsistencies, significantly impacting data reliability and operational efficiency.
Key issues included:
Product descriptions lacked consistent language and formatting, complicating manual classification.
Human classification was slow, error-prone, and resource-intensive.
The client sought to automate classification across multiple asset types with high accuracy to support downstream analytics.
To automatically classify unstructured healthcare product descriptions to reduce manual sorting, improve downstream analytics and compliance.
DRC Systems, a leading AI/ML development company, designed and delivered a custom Machine Learning classification model, integrated into a web application, to automate the categorization of healthcare and pharmaceutical data with high accuracy.
Solution Features:
Applied and benchmarked Support Vector Machine (SVM) and Random Forest classifiers using labeled historical data.
Used NLP models to process, clean, tokenize, and vectorize medical and commercial terms for model training.
Incorporated EasyOCR and Pytesseract for processing image-based product records and extending coverage beyond text-based data.
Delivered a simple, intuitive, and highly interactive web application enabling data upload, classification visualization, manual review, and export.
Achieved 89% model accuracy across pharmaceutical data
Reduced manual sorting effort by over 70%
Enabled large-scale classification in minutes
Delivered clean, categorized data for reporting and analytics
Support Vector Machine (SVM), Random Forest
Python (Pandas, NLTK), Scikitlearn
Pytesseract, EasyOCR
Python with Flask
The solution enabled rapid scaling of product data management across departments and set the foundation for broader AI adoption. By automating the categorization of complex pharmaceutical product descriptions, DRC Systems significantly streamlined operational workflows, minimized manual effort, and enhanced the accuracy and accessibility of enterprise data across departments. The AI-powered accurate classification also helped the client support regulatory and internal compliance efforts.
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