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

Desmond Chun Hwee Teo (), Yiting Huang, Sreemanee Raaj Dorajoo, Michelle Sau Yuen Ng, Chih Tzer Choong, Doris Sock Tin Phuah, Dorothy Hooi Myn Tan, Filina Meixuan Tan, Huilin Huang, Maggie Siok Hwee Tan, Suan Tian Koh, Jalene Wang Woon Poh and Pei San Ang
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Desmond Chun Hwee Teo: Health Sciences Authority
Yiting Huang: Health Sciences Authority
Sreemanee Raaj Dorajoo: Health Sciences Authority
Michelle Sau Yuen Ng: Health Sciences Authority
Chih Tzer Choong: Health Sciences Authority
Doris Sock Tin Phuah: Health Sciences Authority
Dorothy Hooi Myn Tan: Health Sciences Authority
Filina Meixuan Tan: Health Sciences Authority
Huilin Huang: Health Sciences Authority
Maggie Siok Hwee Tan: Health Sciences Authority
Suan Tian Koh: Health Sciences Authority
Jalene Wang Woon Poh: Health Sciences Authority
Pei San Ang: Health Sciences Authority

Drug Safety, 2023, vol. 46, issue 10, No 5, 975-989

Abstract: Abstract 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. Methods A combined machine learning algorithm with a keyword-based model was developed to classify quality issues using text relating to substandard medicines (CISTERM). The nature of issues for product defect cases were classified based on Medical Dictionary for Regulatory Activities–Health Sciences Authority (MedDRA–HSA) lowest-level terms. Results Product defect cases received from January 2010 to December 2021 were used for training (n = 11,082) and for testing (n = 2771). The machine learning model achieved a good recall (precision) of 92% (96%) for ‘Product adulterated and/or contains prohibited substance’, 86% (90%) for ‘Out of specification or out of trend test result’ and 90% (91%) for ‘Manufacturing non-compliance’. Conclusion Post-market surveillance of substandard medicines remains a key activity for drug regulatory authorities. A combined machine learning algorithm with keyword-based model can help to prioritise the review of product quality defect issues in a timely manner.

Date: 2023
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DOI: 10.1007/s40264-023-01339-8

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