Automated Classification of Quality Defect Issues Relating to Substandard Medicines Using a Hybrid Machine Learning and Rule-Based Approach.

Background And Objective: Substandard medicines can lead to serious safety issues affecting public health; however, the nature of such issues can be widely heterogeneous. Health product regulators seek to prioritise critical product quality defects for review to ensure that prompt risk mitigation measures are taken. This study aims to classify the nature of issues for substandard medicines using machine learning to augment a risk-based and timely review of cases.

A risk classification model for prioritising the management of quality issues relating to substandard medicines in Singapore.

Background: Monitoring for substandard medicines by regulatory agencies is a key post-market surveillance activity. It is important to prioritise critical product defects for review to ensure that prompt risk mitigation actions are taken.

Methods: A regulatory risk impact prioritisation model for product defects (RISMED) with 11 factors considering the seriousness and extent of impact of a defect was developed.

Augmenting Product Defect Surveillance Through Web Crawling and Machine Learning in Singapore.

Introduction: Substandard medicines are medicines that fail to meet their quality standards and/or specifications. Substandard medicines can lead to serious safety issues affecting public health. With the increasing number of pharmaceuticals and the complexity of the pharmaceutical manufacturing supply chain, monitoring for substandard medicines via manual environmental scanning can be laborious and time consuming.