An Integrated System of Drug Matching and Abnormal Approval Number Correction

TL;DR

This paper presents an integrated system for matching drug products from multiple data sources and correcting inconsistent approval numbers, achieving high accuracy to support online pharmacy operations during increased demand.

Contribution

The paper introduces a novel integrated system combining drug matching and approval number correction using a Naive-Bayes classifier, addressing data inconsistency challenges in e-Commerce pharmacy platforms.

Findings

Achieved 98.3% drug matching accuracy

Attained 99.2% precision in matching

Reached 97.5% recall in drug identification

Abstract

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