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 mea...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Teo, Desmond Chun Hwee [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Drug safety - Berlin [u.a.] : Springer, 1990, 46(2023), 10 vom: 30. Sept., Seite 975-989 |
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| Übergeordnetes Werk: |
volume:46 ; year:2023 ; number:10 ; day:30 ; month:09 ; pages:975-989 |
| Links: |
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| DOI / URN: |
10.1007/s40264-023-01339-8 |
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| Katalog-ID: |
SPR053452453 |
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| 520 | |a 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. | ||
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| 700 | 1 | |a Ng, Michelle Sau Yuen |0 (orcid)0000-0002-0841-3016 |4 aut | |
| 700 | 1 | |a Choong, Chih Tzer |0 (orcid)0000-0002-5272-5439 |4 aut | |
| 700 | 1 | |a Phuah, Doris Sock Tin |0 (orcid)0000-0002-7400-574X |4 aut | |
| 700 | 1 | |a Tan, Dorothy Hooi Myn |0 (orcid)0000-0003-3204-6786 |4 aut | |
| 700 | 1 | |a Tan, Filina Meixuan |0 (orcid)0000-0002-3789-4672 |4 aut | |
| 700 | 1 | |a Huang, Huilin |4 aut | |
| 700 | 1 | |a Tan, Maggie Siok Hwee |0 (orcid)0000-0003-2301-3632 |4 aut | |
| 700 | 1 | |a Koh, Suan Tian |0 (orcid)0000-0001-5529-3793 |4 aut | |
| 700 | 1 | |a Poh, Jalene Wang Woon |0 (orcid)0000-0002-2126-2144 |4 aut | |
| 700 | 1 | |a Ang, Pei San |0 (orcid)0000-0001-7484-5514 |4 aut | |
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10.1007/s40264-023-01339-8 doi (DE-627)SPR053452453 (SPR)s40264-023-01339-8-e DE-627 ger DE-627 rakwb eng Teo, Desmond Chun Hwee verfasserin (orcid)0000-0001-7641-5462 aut Automated Classification of Quality Defect Issues Relating to Substandard Medicines Using a Hybrid Machine Learning and Rule-Based Approach 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. 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. Huang, Yiting (orcid)0009-0005-9254-2089 aut Dorajoo, Sreemanee Raaj (orcid)0000-0002-9613-6994 aut Ng, Michelle Sau Yuen (orcid)0000-0002-0841-3016 aut Choong, Chih Tzer (orcid)0000-0002-5272-5439 aut Phuah, Doris Sock Tin (orcid)0000-0002-7400-574X aut Tan, Dorothy Hooi Myn (orcid)0000-0003-3204-6786 aut Tan, Filina Meixuan (orcid)0000-0002-3789-4672 aut Huang, Huilin aut Tan, Maggie Siok Hwee (orcid)0000-0003-2301-3632 aut Koh, Suan Tian (orcid)0000-0001-5529-3793 aut Poh, Jalene Wang Woon (orcid)0000-0002-2126-2144 aut Ang, Pei San (orcid)0000-0001-7484-5514 aut Enthalten in Drug safety Berlin [u.a.] : Springer, 1990 46(2023), 10 vom: 30. Sept., Seite 975-989 (DE-627)320630714 (DE-600)2023894-0 1179-1942 nnns volume:46 year:2023 number:10 day:30 month:09 pages:975-989 https://dx.doi.org/10.1007/s40264-023-01339-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 46 2023 10 30 09 975-989 |
| spelling |
10.1007/s40264-023-01339-8 doi (DE-627)SPR053452453 (SPR)s40264-023-01339-8-e DE-627 ger DE-627 rakwb eng Teo, Desmond Chun Hwee verfasserin (orcid)0000-0001-7641-5462 aut Automated Classification of Quality Defect Issues Relating to Substandard Medicines Using a Hybrid Machine Learning and Rule-Based Approach 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. 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. Huang, Yiting (orcid)0009-0005-9254-2089 aut Dorajoo, Sreemanee Raaj (orcid)0000-0002-9613-6994 aut Ng, Michelle Sau Yuen (orcid)0000-0002-0841-3016 aut Choong, Chih Tzer (orcid)0000-0002-5272-5439 aut Phuah, Doris Sock Tin (orcid)0000-0002-7400-574X aut Tan, Dorothy Hooi Myn (orcid)0000-0003-3204-6786 aut Tan, Filina Meixuan (orcid)0000-0002-3789-4672 aut Huang, Huilin aut Tan, Maggie Siok Hwee (orcid)0000-0003-2301-3632 aut Koh, Suan Tian (orcid)0000-0001-5529-3793 aut Poh, Jalene Wang Woon (orcid)0000-0002-2126-2144 aut Ang, Pei San (orcid)0000-0001-7484-5514 aut Enthalten in Drug safety Berlin [u.a.] : Springer, 1990 46(2023), 10 vom: 30. Sept., Seite 975-989 (DE-627)320630714 (DE-600)2023894-0 1179-1942 nnns volume:46 year:2023 number:10 day:30 month:09 pages:975-989 https://dx.doi.org/10.1007/s40264-023-01339-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 46 2023 10 30 09 975-989 |
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10.1007/s40264-023-01339-8 doi (DE-627)SPR053452453 (SPR)s40264-023-01339-8-e DE-627 ger DE-627 rakwb eng Teo, Desmond Chun Hwee verfasserin (orcid)0000-0001-7641-5462 aut Automated Classification of Quality Defect Issues Relating to Substandard Medicines Using a Hybrid Machine Learning and Rule-Based Approach 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. 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. Huang, Yiting (orcid)0009-0005-9254-2089 aut Dorajoo, Sreemanee Raaj (orcid)0000-0002-9613-6994 aut Ng, Michelle Sau Yuen (orcid)0000-0002-0841-3016 aut Choong, Chih Tzer (orcid)0000-0002-5272-5439 aut Phuah, Doris Sock Tin (orcid)0000-0002-7400-574X aut Tan, Dorothy Hooi Myn (orcid)0000-0003-3204-6786 aut Tan, Filina Meixuan (orcid)0000-0002-3789-4672 aut Huang, Huilin aut Tan, Maggie Siok Hwee (orcid)0000-0003-2301-3632 aut Koh, Suan Tian (orcid)0000-0001-5529-3793 aut Poh, Jalene Wang Woon (orcid)0000-0002-2126-2144 aut Ang, Pei San (orcid)0000-0001-7484-5514 aut Enthalten in Drug safety Berlin [u.a.] : Springer, 1990 46(2023), 10 vom: 30. Sept., Seite 975-989 (DE-627)320630714 (DE-600)2023894-0 1179-1942 nnns volume:46 year:2023 number:10 day:30 month:09 pages:975-989 https://dx.doi.org/10.1007/s40264-023-01339-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 46 2023 10 30 09 975-989 |
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10.1007/s40264-023-01339-8 doi (DE-627)SPR053452453 (SPR)s40264-023-01339-8-e DE-627 ger DE-627 rakwb eng Teo, Desmond Chun Hwee verfasserin (orcid)0000-0001-7641-5462 aut Automated Classification of Quality Defect Issues Relating to Substandard Medicines Using a Hybrid Machine Learning and Rule-Based Approach 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. 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. Huang, Yiting (orcid)0009-0005-9254-2089 aut Dorajoo, Sreemanee Raaj (orcid)0000-0002-9613-6994 aut Ng, Michelle Sau Yuen (orcid)0000-0002-0841-3016 aut Choong, Chih Tzer (orcid)0000-0002-5272-5439 aut Phuah, Doris Sock Tin (orcid)0000-0002-7400-574X aut Tan, Dorothy Hooi Myn (orcid)0000-0003-3204-6786 aut Tan, Filina Meixuan (orcid)0000-0002-3789-4672 aut Huang, Huilin aut Tan, Maggie Siok Hwee (orcid)0000-0003-2301-3632 aut Koh, Suan Tian (orcid)0000-0001-5529-3793 aut Poh, Jalene Wang Woon (orcid)0000-0002-2126-2144 aut Ang, Pei San (orcid)0000-0001-7484-5514 aut Enthalten in Drug safety Berlin [u.a.] : Springer, 1990 46(2023), 10 vom: 30. Sept., Seite 975-989 (DE-627)320630714 (DE-600)2023894-0 1179-1942 nnns volume:46 year:2023 number:10 day:30 month:09 pages:975-989 https://dx.doi.org/10.1007/s40264-023-01339-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 46 2023 10 30 09 975-989 |
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10.1007/s40264-023-01339-8 doi (DE-627)SPR053452453 (SPR)s40264-023-01339-8-e DE-627 ger DE-627 rakwb eng Teo, Desmond Chun Hwee verfasserin (orcid)0000-0001-7641-5462 aut Automated Classification of Quality Defect Issues Relating to Substandard Medicines Using a Hybrid Machine Learning and Rule-Based Approach 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. 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. Huang, Yiting (orcid)0009-0005-9254-2089 aut Dorajoo, Sreemanee Raaj (orcid)0000-0002-9613-6994 aut Ng, Michelle Sau Yuen (orcid)0000-0002-0841-3016 aut Choong, Chih Tzer (orcid)0000-0002-5272-5439 aut Phuah, Doris Sock Tin (orcid)0000-0002-7400-574X aut Tan, Dorothy Hooi Myn (orcid)0000-0003-3204-6786 aut Tan, Filina Meixuan (orcid)0000-0002-3789-4672 aut Huang, Huilin aut Tan, Maggie Siok Hwee (orcid)0000-0003-2301-3632 aut Koh, Suan Tian (orcid)0000-0001-5529-3793 aut Poh, Jalene Wang Woon (orcid)0000-0002-2126-2144 aut Ang, Pei San (orcid)0000-0001-7484-5514 aut Enthalten in Drug safety Berlin [u.a.] : Springer, 1990 46(2023), 10 vom: 30. Sept., Seite 975-989 (DE-627)320630714 (DE-600)2023894-0 1179-1942 nnns volume:46 year:2023 number:10 day:30 month:09 pages:975-989 https://dx.doi.org/10.1007/s40264-023-01339-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 46 2023 10 30 09 975-989 |
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Teo, Desmond Chun Hwee @@aut@@ Huang, Yiting @@aut@@ Dorajoo, Sreemanee Raaj @@aut@@ Ng, Michelle Sau Yuen @@aut@@ Choong, Chih Tzer @@aut@@ Phuah, Doris Sock Tin @@aut@@ Tan, Dorothy Hooi Myn @@aut@@ Tan, Filina Meixuan @@aut@@ Huang, Huilin @@aut@@ Tan, Maggie Siok Hwee @@aut@@ Koh, Suan Tian @@aut@@ Poh, Jalene Wang Woon @@aut@@ Ang, Pei San @@aut@@ |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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. 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automated classification of quality defect issues relating to substandard medicines using a hybrid machine learning and rule-based approach |
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Automated Classification of Quality Defect Issues Relating to Substandard Medicines Using a Hybrid Machine Learning and Rule-Based Approach |
| 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. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
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. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
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. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Automated Classification of Quality Defect Issues Relating to Substandard Medicines Using a Hybrid Machine Learning and Rule-Based Approach |
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Huang, Yiting Dorajoo, Sreemanee Raaj Ng, Michelle Sau Yuen Choong, Chih Tzer Phuah, Doris Sock Tin Tan, Dorothy Hooi Myn Tan, Filina Meixuan Huang, Huilin Tan, Maggie Siok Hwee Koh, Suan Tian Poh, Jalene Wang Woon Ang, Pei San |
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| score |
7.4031515 |