{"id":16381,"date":"2025-11-20T12:50:51","date_gmt":"2025-11-20T07:20:51","guid":{"rendered":"https:\/\/opso.dev.drcsystems.ooo\/wordpress-projects\/drc2025\/?post_type=case_study&p=13925"},"modified":"2026-05-01T17:28:47","modified_gmt":"2026-05-01T11:58:47","slug":"ai-ml-case-studies-classification-model-for-pharmaceutical-descriptions","status":"publish","type":"case_study","link":"https:\/\/opso.dev.drcsystems.ooo\/wordpress-projects\/drc2025\/case_study\/ai-ml-case-studies-classification-model-for-pharmaceutical-descriptions\/","title":{"rendered":"AI-ML Case Studies \u2013 Classification Model for Pharmaceutical Descriptions"},"content":{"rendered":"
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Driving Efficiency in Pharma Product Management with an AI-Powered Category Classification Solution<\/h1>\n<\/div>\n<\/section>\n
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Client Overview<\/h2>\n

A leading global organization in the healthcare and pharmaceutical sector needed a scalable solution to classify thousands of unstructured product descriptions\u2014spanning medical devices, pharmaceuticals, consumables, and supplements\u2014into accurate, predefined categories. This classification was critical to support valuation processes, regulatory compliance, and streamlined operational workflows.<\/p>\n<\/div>\n<\/div>\n<\/section>\n

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Business Challenge<\/h2>\n

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.<\/p>\n

Key issues included:<\/p>\n

Unstructured and Ambiguous Data :<\/h5>\n

Product descriptions lacked consistent language and formatting, complicating manual classification.<\/p>\n

High Dependency on Manual Sorting :<\/h5>\n

Human classification was slow, error-prone, and resource-intensive.<\/p>\n

Need for Scalable Automation :<\/h5>\n

The client sought to automate classification across multiple asset types with high accuracy to support downstream analytics.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n

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\"Case<\/div>\n<\/div>\n
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What Client Needed<\/h2>\n

To automatically classify unstructured healthcare product descriptions to reduce manual sorting, improve downstream analytics and compliance.<\/p>\n

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  • Accurately categorize unstructured healthcare asset descriptions.<\/li>\n
  • Reduce manual effort and increase throughput<\/li>\n
  • Provide consistent tagging for improved reporting, valuation, and informed decision-making<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n
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    What We Built<\/h2>\n

    DRC Systems, a leading AI\/ML development company, designed and delivered a custom Machine Learning classification model,<\/strong> integrated into a web application, to automate the categorization of healthcare and pharmaceutical data with high accuracy.<\/p>\n

    Solution Features:<\/strong><\/p>\n<\/div>\n

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      Model Training & Evaluation<\/h6>\n

      Applied and benchmarked Support Vector Machine (SVM) and Random Forest classifiers using labeled historical data.<\/p>\n<\/li>\n

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      Natural Language Processing (NLP)<\/h6>\n

      Used NLP models to process, clean, tokenize, and vectorize medical and commercial terms for model training.<\/p>\n<\/li>\n

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      OCR Integration<\/h6>\n

      Incorporated EasyOCR and Pytesseract for processing image-based product records and extending coverage beyond text-based data.<\/p>\n<\/li>\n

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      Web Interface for Review & Export<\/h6>\n

      Delivered a simple, intuitive, and highly interactive web application enabling data upload, classification visualization, manual review, and export.<\/p>\n<\/li>\n<\/ul>\n<\/div>\n<\/div>\n

      \n
      \"Case<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n
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      \"Case<\/div>\n
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      Business Impact<\/h2>\n<\/div>\n
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      Classification Accuracy<\/h5>\n

      Achieved 89% model accuracy<\/strong> across pharmaceutical data<\/p>\n<\/div>\n<\/div>\n

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      Operational Efficiency<\/h5>\n

      Reduced manual sorting effort by over 70%<\/strong><\/p>\n<\/div>\n<\/div>\n

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      Faster Data Processing<\/h5>\n

      Enabled large-scale classification in minutes<\/p>\n<\/div>\n<\/div>\n

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      Improved Decision-Making<\/h5>\n

      Delivered clean, categorized data for reporting and analytics<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n

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      ML Algorithms for Classification<\/h5>\n

      Support Vector Machine (SVM), Random Forest<\/p>\n<\/div>\n

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      Text Preprocessing<\/h5>\n

      Python (Pandas, NLTK), Scikitlearn<\/p>\n<\/div>\n<\/div>\n

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      OCR Integration for Image-Based Data<\/h5>\n

      Pytesseract, EasyOCR<\/p>\n<\/div>\n

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      Web Application for Review and Export<\/h5>\n

      Python with Flask<\/p>\n<\/div>\n<\/div>\n<\/div>\n

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      Tech Stack<\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n
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      Conclusion<\/h2>\n

      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.<\/p>\n<\/div>\n<\/div>\n

      \"Case<\/div>\n<\/section>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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A cloud & SaaS-based solution with single integrated system providing real-time insights on customer service, compliance, operation and management.<\/p>\n","protected":false},"featured_media":16422,"template":"","meta":{"_acf_changed":false,"inline_featured_image":false},"case-study-tags":[],"class_list":["post-16381","case_study","type-case_study","status-publish","has-post-thumbnail","hentry","case_study_cat-financial-services","case_study_tech-php","case_study_region-uae"],"acf":[],"yoast_head":"\nAI-ML Case Studies \u2013 Classification Model for Pharmaceutical Descriptions - DRC Systems<\/title>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"4 minutes\" \/>\n<script type=\"application\/ld+json\" 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