Using Machine Learning for Pharmacovigilance: A Systematic Review
Pharmacovigilance is a science that involves the ongoing monitoring of adverse drug reactions to existing medicines. Traditional approaches in this field can be expensive and time-consuming. The application of natural language processing (NLP) to analyze user-generated content is hypothesized as an...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Pharmaceutics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4923/14/2/266 |
| _version_ | 1797477231266627584 |
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| author | Patrick Pilipiec Marcus Liwicki András Bota |
| author_facet | Patrick Pilipiec Marcus Liwicki András Bota |
| author_sort | Patrick Pilipiec |
| collection | DOAJ |
| description | Pharmacovigilance is a science that involves the ongoing monitoring of adverse drug reactions to existing medicines. Traditional approaches in this field can be expensive and time-consuming. The application of natural language processing (NLP) to analyze user-generated content is hypothesized as an effective supplemental source of evidence. In this systematic review, a broad and multi-disciplinary literature search was conducted involving four databases. A total of 5318 publications were initially found. Studies were considered relevant if they reported on the application of NLP to understand user-generated text for pharmacovigilance. A total of 16 relevant publications were included in this systematic review. All studies were evaluated to have medium reliability and validity. For all types of drugs, 14 publications reported positive findings with respect to the identification of adverse drug reactions, providing consistent evidence that natural language processing can be used effectively and accurately on user-generated textual content that was published to the Internet to identify adverse drug reactions for the purpose of pharmacovigilance. The evidence presented in this review suggest that the analysis of textual data has the potential to complement the traditional system of pharmacovigilance. |
| first_indexed | 2024-03-09T21:14:43Z |
| format | Article |
| id | doaj.art-1fb6d9b57c25400cb34d18b6e6a82813 |
| institution | Directory Open Access Journal |
| issn | 1999-4923 |
| language | English |
| last_indexed | 2024-03-09T21:14:43Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Pharmaceutics |
| spelling | doaj.art-1fb6d9b57c25400cb34d18b6e6a828132023-11-23T21:36:23ZengMDPI AGPharmaceutics1999-49232022-01-0114226610.3390/pharmaceutics14020266Using Machine Learning for Pharmacovigilance: A Systematic ReviewPatrick Pilipiec0Marcus Liwicki1András Bota2Embedded Intelligent Systems Lab, Department of Computer Science Electrical and Space Engineering, Luleå University of Technology, 97187 Luleå, SwedenEmbedded Intelligent Systems Lab, Department of Computer Science Electrical and Space Engineering, Luleå University of Technology, 97187 Luleå, SwedenEmbedded Intelligent Systems Lab, Department of Computer Science Electrical and Space Engineering, Luleå University of Technology, 97187 Luleå, SwedenPharmacovigilance is a science that involves the ongoing monitoring of adverse drug reactions to existing medicines. Traditional approaches in this field can be expensive and time-consuming. The application of natural language processing (NLP) to analyze user-generated content is hypothesized as an effective supplemental source of evidence. In this systematic review, a broad and multi-disciplinary literature search was conducted involving four databases. A total of 5318 publications were initially found. Studies were considered relevant if they reported on the application of NLP to understand user-generated text for pharmacovigilance. A total of 16 relevant publications were included in this systematic review. All studies were evaluated to have medium reliability and validity. For all types of drugs, 14 publications reported positive findings with respect to the identification of adverse drug reactions, providing consistent evidence that natural language processing can be used effectively and accurately on user-generated textual content that was published to the Internet to identify adverse drug reactions for the purpose of pharmacovigilance. The evidence presented in this review suggest that the analysis of textual data has the potential to complement the traditional system of pharmacovigilance.https://www.mdpi.com/1999-4923/14/2/266pharmacovigilanceadverse drug reactionsADRscomputational linguisticsmachine learningpublic health |
| spellingShingle | Patrick Pilipiec Marcus Liwicki András Bota Using Machine Learning for Pharmacovigilance: A Systematic Review Pharmaceutics pharmacovigilance adverse drug reactions ADRs computational linguistics machine learning public health |
| title | Using Machine Learning for Pharmacovigilance: A Systematic Review |
| title_full | Using Machine Learning for Pharmacovigilance: A Systematic Review |
| title_fullStr | Using Machine Learning for Pharmacovigilance: A Systematic Review |
| title_full_unstemmed | Using Machine Learning for Pharmacovigilance: A Systematic Review |
| title_short | Using Machine Learning for Pharmacovigilance: A Systematic Review |
| title_sort | using machine learning for pharmacovigilance a systematic review |
| topic | pharmacovigilance adverse drug reactions ADRs computational linguistics machine learning public health |
| url | https://www.mdpi.com/1999-4923/14/2/266 |
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