The Role of Advanced Technologies Supplemented with Traditional Methods in Pharmacovigilance Sciences
Copyright© Bentham Science Publishers; For any queries, please email at epubbenthamscience.net..
BACKGROUND: The immediate automatic systemic monitoring and reporting of adverse drug reactions, improving the efficacy is the utmost need of the medical informatics community. The venturing of advanced digital technologies into the health sector has opened new avenues for rapid monitoring. In recent years, data shared through social media, mobile apps, and other social websites has increased manifolds requiring data mining techniques.
OBJECTIVE: The objective of this report is to highlight the role of advanced technologies together with the traditional methods to proactively aid in the early detection of adverse drug reactions concerned with drug safety and pharmacovigilance.
METHODS: A thorough search was conducted on papers and patents regarding pharmacovigilance. All articles with respect to the relevant subject were explored and mined from public repositories such as Pubmed, Google Scholar, Springer, ScienceDirect (Elsevier), Web of Science, etc. Results: The European Union's Innovative Medicines Initiative WEB-RADR project has emphasized the development of mobile applications and social media data for reporting adverse effects. Only relevant data has to be captured through the data mining algorithms (DMAs) as it plays an important role in timely prediction of risk with high accuracy using two popular approaches; the frequentist and Bayesian approach. Pharmacovigilance at the pre-marketing stage is useful for the prediction of adverse drug reactions in the early developmental stage of a drug. Later, post-marketing safety reports and clinical data reports are important to be monitored through electronic health records, prescription-event monitoring, spontaneous reporting databases, etc. Conclusion: The advanced technologies supplemented with traditional technologies are the need of the hour for evaluating a product's risk profile and reducing risk in population especially with comorbid conditions and on concomitant medications.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:15 |
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| Enthalten in: |
Recent patents on biotechnology - 15(2021), 1 vom: 21., Seite 34-50 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Satwika, Mandali V [VerfasserIn] |
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| Links: |
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| Themen: |
Adverse drug reaction |
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| Anmerkungen: |
Date Completed 21.06.2021 Date Revised 21.06.2021 published: Print Citation Status MEDLINE |
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| doi: |
10.2174/1872208314666201021162704 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM316560316 |
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| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright© Bentham Science Publishers; For any queries, please email at epubbenthamscience.net. | ||
| 520 | |a BACKGROUND: The immediate automatic systemic monitoring and reporting of adverse drug reactions, improving the efficacy is the utmost need of the medical informatics community. The venturing of advanced digital technologies into the health sector has opened new avenues for rapid monitoring. In recent years, data shared through social media, mobile apps, and other social websites has increased manifolds requiring data mining techniques | ||
| 520 | |a OBJECTIVE: The objective of this report is to highlight the role of advanced technologies together with the traditional methods to proactively aid in the early detection of adverse drug reactions concerned with drug safety and pharmacovigilance | ||
| 520 | |a METHODS: A thorough search was conducted on papers and patents regarding pharmacovigilance. All articles with respect to the relevant subject were explored and mined from public repositories such as Pubmed, Google Scholar, Springer, ScienceDirect (Elsevier), Web of Science, etc. Results: The European Union's Innovative Medicines Initiative WEB-RADR project has emphasized the development of mobile applications and social media data for reporting adverse effects. Only relevant data has to be captured through the data mining algorithms (DMAs) as it plays an important role in timely prediction of risk with high accuracy using two popular approaches; the frequentist and Bayesian approach. Pharmacovigilance at the pre-marketing stage is useful for the prediction of adverse drug reactions in the early developmental stage of a drug. Later, post-marketing safety reports and clinical data reports are important to be monitored through electronic health records, prescription-event monitoring, spontaneous reporting databases, etc. Conclusion: The advanced technologies supplemented with traditional technologies are the need of the hour for evaluating a product's risk profile and reducing risk in population especially with comorbid conditions and on concomitant medications | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Review | |
| 650 | 4 | |a Adverse drug reaction | |
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| 650 | 4 | |a pharmacovigilance | |
| 650 | 4 | |a signal detection | |
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| 700 | 1 | |a Jaiswal, Varun |e verfasserin |4 aut | |
| 700 | 1 | |a Asha, Syed |e verfasserin |4 aut | |
| 700 | 1 | |a Pal, Tarun |e verfasserin |4 aut | |
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