Active Monitoring of Adverse Drug Reactions with Neural Network Technology

Wu, Tao; Gao, Changchun; Lin, Jun; Zha, Jia-Ling

doi:10.4103/0366-6999.207468

Cited by 2 publications

(1 citation statement)

References 11 publications

(18 reference statements)

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“…Active surveillance is promoted for early detection of ADRs and signals. 11,12 Government of India has also initiated active DSM in 2015 for second line and newer drugs used in multi drug resistant (MDR) and extremely drug resistant (XDR) cases of tuberculosis (TB) but there are still negligible data regarding comparison of these two methods of ADR surveillance in terms of actual yield and false reporting. 13 The objective of the present research was to compare active and passive ADR reporting methods with special focus on false reporting rate in category I tubercular patients.…”

Section: Methodsmentioning

confidence: 99%

A comparative study of active and passive adverse drug reaction reporting systems in terms of false reporting rate

Bansal¹,

Agrawal²,

Sharma³

et al. 2020

Scripta Medica

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Background: World Health Organisation Uppsala Monitoring Centre (WHO-UMC) was set up in 1968 to collect Adverse Drug Reactions (ADRs) periodically for all drugs across the globe. It identifies two main approaches to pharmacovigilance: active (intensive) and passive (spontaneous). However, very few studies are available to compare these two methods of adverse drug reaction reporting. Methods: A prospective observational study was done on 303 newly diagnosed patients with tuberculosis receiving directly observed therapy short-course (DOTS) in the Sawai Man Singh (SMS) Hospital, Jaipur between 1 January 2019 and 31 December 2019. They were randomly divided into groups A (150 patients) and B (153 patients). Group A patients were followed actively at fixed intervals of time for ADRs till next six months through electronic conversation or personal interview. Group B patients were required to report spontaneously for any ADRs to pharmacovigilance centre. After data collection causality assessment was done using the WHO-UMC scale to identify false reporting and finally results were analysed statistically by means of the t-test using Minitab 14 software Pennsylvania, USA. Results: 153 ADRs were reported in active and 39 in passive group. Hence the yield of ADR was four times more in active method. After causality assessment, 31 in group A and 12 in group B were found to be falsely related (unlikely) to antitubercular drugs. Two sample t-test revealed active method reported more false ADR (p = 0.005). Conclusion: Although active method of surveillance identifies more ADRs than passive method, it is also more prone to false reporting. Hence its benefits should be weighed against its cost before adopting it for countries with limited resources.

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Section: Methodsmentioning

confidence: 99%

A comparative study of active and passive adverse drug reaction reporting systems in terms of false reporting rate

Bansal¹,

Agrawal²,

Sharma³

et al. 2020

Scripta Medica

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Perspective Chapter: Application of Artificial Intelligence in Pharmacovigilance Practices

Gamaleldin,

Abdelhalim,

Abraham

2024

Pharmacovigilance - Facts, Challenges, Limitations and Opportunity [Working Title]

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Patient exposure to medication during drug therapy makes the need for pharmacovigilance an integral part of patient safety. It is also followed by how the medicine enter into market phases. Pharmacovigilance systems and post-market surveillance reports ensure that regulatory authorities could monitor and assess adverse drug reactions in real time. Through the progress and innovations in artificial intelligence, which is getting its applications widely implemented in several healthcare domains these days, this has brought systems that could give the results quickly with high-level accuracy compared to those of humans. These systems are being employed to reduce adverse drug reactions (ADR), pick up safety signals swiftly, and serve as a directional tool to enable qualified entities of stakeholders, regulatory authorities, and pharmaceutical companies for the subsequent informed decision making. Artificial intelligence (AI) systems are changing healthcare and pharmaceuticals for a variety of reasons, including the speed at which they can be quickly advanced. Healthcare providers, regulatory agencies, and drug companies need to employ such systems in their pharmacovigilance practices. This chapter covers the application of artificial intelligence in pharmacovigilance systems and provides overview on integration of pharmacovigilance with AI for dummies. This chapter reviews the advantages and drawbacks of using AI in pharmacovigilance, creating regulatory and safety guidelines for drug safety, pharmaceutical product development, clinical trial design, estimate of product costs, and the perspective on future use of AI in pharmacovigilance.

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Active Monitoring of Adverse Drug Reactions with Neural Network Technology

Cited by 2 publications

References 11 publications

A comparative study of active and passive adverse drug reaction reporting systems in terms of false reporting rate

A comparative study of active and passive adverse drug reaction reporting systems in terms of false reporting rate

Perspective Chapter: Application of Artificial Intelligence in Pharmacovigilance Practices

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