Artificial Intelligence and Data Mining for the Pharmacovigilance of Drug–Drug Interactions

Hauben, Manfred

doi:10.1016/j.clinthera.2023.01.002

Cited by 14 publications

(5 citation statements)

References 70 publications

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“…Artificial intelligence algorithms need to be trained with large quantities of high-quality data. The technical challenges of artificial intelligence-based pharmacovigilance, particularly in LMICs, are the lack of high-quality databases, insufficient human resources, weak artificial intelligence technology, data sharing and privacy challenges, transparency of algorithms, interoperability across multiple platforms and insufficient support from governments 28 33 71 78–80…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, systems ought to avoid over-reliance on automated standalone software programs as they could generate false signals. Instead, holistic methods which include manual review alongside automated processes should be preferred 33 34. The objective of this scoping review is to provide a comprehensive overview of duplication in pharmacovigilance databases on a global scale.…”

Section: Introductionmentioning

confidence: 99%

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Navigating duplication in pharmacovigilance databases: a scoping review

Kiguba,

Isabirye,

Mayengo

et al. 2024

BMJ Open

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ObjectivesPharmacovigilance databases play a critical role in monitoring drug safety. The duplication of reports in pharmacovigilance databases, however, undermines their data integrity. This scoping review sought to provide a comprehensive understanding of duplication in pharmacovigilance databases worldwide.DesignA scoping review.Data sourcesReviewers comprehensively searched the literature in PubMed, Web of Science, Wiley Online Library, EBSCOhost, Google Scholar and other relevant websites.Eligibility criteriaPeer-reviewed publications and grey literature, without language restriction, describing duplication and/or methods relevant to duplication in pharmacovigilance databases from inception to 1 September 2023.Data extraction and synthesisWe used the Joanna Briggs Institute guidelines for scoping reviews and conformed with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews. Two reviewers independently screened titles, abstracts and full texts. One reviewer extracted the data and performed descriptive analysis, which the second reviewer assessed. Disagreements were resolved by discussion and consensus or in consultation with a third reviewer.ResultsWe screened 22 745 unique titles and 156 were eligible for full-text review. Of the 156 titles, 58 (47 peer-reviewed; 11 grey literature) fulfilled the inclusion criteria for the scoping review. Included titles addressed the extent (5 papers), prevention strategies (15 papers), causes (32 papers), detection methods (25 papers), management strategies (24 papers) and implications (14 papers) of duplication in pharmacovigilance databases. The papers overlapped, discussing more than one field. Advances in artificial intelligence, particularly natural language processing, hold promise in enhancing the efficiency and precision of deduplication of large and complex pharmacovigilance databases.ConclusionDuplication in pharmacovigilance databases compromises risk assessment and decision-making, potentially threatening patient safety. Therefore, efficient duplicate prevention, detection and management are essential for more reliable pharmacovigilance data. To minimise duplication, consistent use of worldwide unique identifiers as the key case identifiers is recommended alongside recent advances in artificial intelligence.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Navigating duplication in pharmacovigilance databases: a scoping review

Kiguba,

Isabirye,

Mayengo

et al. 2024

BMJ Open

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“…The use of technologies that support clinical decision-making and the determination of their contribution to the solution may be the subject of another study. 35…”

Section: Limitationsmentioning

confidence: 99%

Investigation of polypharmacy and potential drug-drug interactions in a group of hospitalized pediatric patients: a single-center study

Akgöl,

Bükülmez

2024

Journal of Health Sciences and Medicine

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Aims: Polypharmacy involves the use of multiple medications to manage one or more clinical conditions. This study aimed to determine the prevalence of polypharmacy and potential drug-drug interactions during hospitalizations in childhood and to investigate the nature of common interactions. Methods: Data for this retrospective cross-sectional observational study were obtained from the hospital database records of pediatric patients admitted to the pediatric department of a university hospital during the first six months of 2020. A total of 601 pediatric prescriptions from 877 hospitalizations involving 2620 medications were examined for drug-drug interactions using the drugs.com/interaction checker tool. Results: Of the evaluated 601 patients, 48.1% were female and 51.9% were male children. The mean age of the hospitalized patients was 4.78±5.2 years, ranging from 0 to 18 years, with a median age of 2 years. The mean length of the hospital stay was 5.5 (min 1-max 56) days. The mean number of prescribed medications per child was 4.38±2.4 (min-max 1-16). Potential interactions were identified in 49.1% of the prescriptions. The prescription rate of antimicrobial treatment for hospitalized patients was 86%, and this group had a high occurrence of major drug-drug interactions (p

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“…As AI evolves, it will play an indispensable role in enhancing drug safety surveillance and risk management, ushering in a new era of proactive and data-driven pharmacovigilance practices. [143,144]…”

Section: Ai Chatgpt Application In Pharmacovigilance Industrymentioning

confidence: 99%

ChatGPT in medicine: A cross-disciplinary systematic review of ChatGPT’s (artificial intelligence) role in research, clinical practice, education, and patient interaction

Fatima,

Shafique,

Alam

et al. 2024

Medicine

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Background: ChatGPT, a powerful AI language model, has gained increasing prominence in medicine, offering potential applications in healthcare, clinical decision support, patient communication, and medical research. This systematic review aims to comprehensively assess the applications of ChatGPT in healthcare education, research, writing, patient communication, and practice while also delineating potential limitations and areas for improvement. Method: Our comprehensive database search retrieved relevant papers from PubMed, Medline and Scopus. After the screening process, 83 studies met the inclusion criteria. This review includes original studies comprising case reports, analytical studies, and editorials with original findings. Result: ChatGPT is useful for scientific research and academic writing, and assists with grammar, clarity, and coherence. This helps non-English speakers and improves accessibility by breaking down linguistic barriers. However, its limitations include probable inaccuracy and ethical issues, such as bias and plagiarism. ChatGPT streamlines workflows and offers diagnostic and educational potential in healthcare but exhibits biases and lacks emotional sensitivity. It is useful in inpatient communication, but requires up-to-date data and faces concerns about the accuracy of information and hallucinatory responses. Conclusion: Given the potential for ChatGPT to transform healthcare education, research, and practice, it is essential to approach its adoption in these areas with caution due to its inherent limitations.

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Artificial Intelligence and Data Mining for the Pharmacovigilance of Drug–Drug Interactions

Cited by 14 publications

References 70 publications

Navigating duplication in pharmacovigilance databases: a scoping review

Navigating duplication in pharmacovigilance databases: a scoping review

Investigation of polypharmacy and potential drug-drug interactions in a group of hospitalized pediatric patients: a single-center study

ChatGPT in medicine: A cross-disciplinary systematic review of ChatGPT’s (artificial intelligence) role in research, clinical practice, education, and patient interaction

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