An Integrated System of Drug Matching and Abnormal Approval Number Correction
This addresses data inconsistency challenges for pharmacy e-commerce platforms, but it is incremental as it applies existing methods like Naive-Bayes to a specific domain.
The paper tackles the problem of inconsistent drug product information from multiple sources in online pharmacy platforms by creating an integrated system for drug matching and approval number correction, achieving 98.3% accuracy, 99.2% precision, and 97.5% recall.
This essay is based on the joint project with 111, Inc. The pharmacy e-Commerce business grows rapidly in recent years with the ever-increasing medical demand during the pandemic. A big challenge for online pharmacy platforms is drug product matching. The e-Commerce platform usually collects drug product information from multiple data sources such as the warehouse or retailers. Therefore, the data format is inconsistent, making it hard to identify and match the same drug product. This paper creates an integrated system for matching drug products from two data sources. Besides, the system would correct some inconsistent drug approval numbers based on a Naive-Bayes drug type (Chinese or Non-Chinese Drug) classifier. Our integrated system achieves 98.3% drug matching accuracy, with 99.2% precision and 97.5% recall