Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review

Syrowatka, Ania; Song, Wenyu; Amato, Mary G.; Foer, Dinah; Edrees, Heba; Co, Zoe; Кузнецова, М. М.; Dulgarian, Sevan; Seger, Diane L.; Simona, Aurélien; Bain, Paul; Jackson, Gretchen Purcell; Rhee, Kyu; Bates, David W.

doi:10.1016/s2589-7500(21)00229-6

Cited by 32 publications

(16 citation statements)

References 87 publications

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“…There are 76 different studies in the literature, 74% of which have emerged in the last 5 years, in which artificial intelligence is used to prevent and detect ADRs from drug discovery to pharmacovigilance in the general population. It was observed that 18% of these studies detected cardiovascular or renal ADRs, which were also frequently observed in our study [ 22 , 23 ]. McMaster et al developed a machine learning algorithm that automatically detects 44.5% of ADRs reported by diagnosis code in predominantly adult patients (AUC: 0.803) [ 21 ].…”

Section: Discussionsupporting

confidence: 87%

An Artificial Intelligence Approach to Support Detection of Neonatal Adverse Drug Reactions Based on Severity and Probability Scores: A New Risk Score as Web-Tool

et al. 2022

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Background: Critically ill neonates are at greater risk for adverse drug reactions (ADRs). The differentiation of ADRs from reactions associated with organ dysfunction/immaturity or genetic variability is difficult. Methods: In this prospective cohort study, each ADR was assessed using newborn-specific severity and probability scales by the clinical pharmacist. Subsequently, a machine learning-based risk score was designed to predict ADR presence in neonates. Results: In 98/412 (23.8%) of (56.3%; male) neonates included, 187 ADRs (0.42 ADR/patient) were determined related to 49 different drugs (37.12%). Drugs identified as high risk were enoxaparin, dexmedetomidine, vinblastine, dornase alfa, etoposide/carboplatin and prednisolone. The independent variables included in the risk score to predict ADR presence, according to the random forest importance criterion, were: systemic hormones (2 points), cardiovascular drugs (3 points), diseases of the circulatory system (1 point), nervous system drugs (1 point), and parenteral nutrition treatment (1 point), (cut-off value: 3 points). This risk score correctly classified 91.1% of the observations in the test set (c-index: 0.914). Conclusions: Using the high-performing risk score specific to neonates, it is expected that high-risk neonatal ADRs can be determined and prevented before they occur. Moreover, the awareness of clinicians of these drugs can be improved with this web-tool, and mitigation strategies (change of drug, dose, treatment duration, etc.) can be considered, based on a benefit-harm relationship for suspected drugs with a newborn-centered approach.

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Section: Discussionsupporting

confidence: 87%

An Artificial Intelligence Approach to Support Detection of Neonatal Adverse Drug Reactions Based on Severity and Probability Scores: A New Risk Score as Web-Tool

et al. 2022

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“…Despite the widespread interest in AI and its application to safety data [ 36 , 37 ], including several review articles [ 14 , 15 ], there are no scoping reviews that critically assess the extent to which PV is poised to be improved by AI under this framework. Previous reviews have focused on specific elements such as NLP techniques for clinical narrative mining in EHRs [ 38 ] or in reducing the frequency or impact of adverse events to patients [ 39 ]. Our review is unique in that it seeks to fill this gap to provide a clearer understanding of how current AI/ML practices and standards in PV align with the critical factors for success identified in adjacent areas such as biology and medicine.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Based on Machine Learning in Pharmacovigilance: A Scoping Review

et al. 2022

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Introduction Artificial intelligence based on machine learning has made large advancements in many fields of science and medicine but its impact on pharmacovigilance is yet unclear. Objective The present study conducted a scoping review of the use of artificial intelligence based on machine learning to understand how it is used for pharmacovigilance tasks, characterize differences with other fields, and identify opportunities to improve pharmacovigilance through the use of machine learning. Design The PubMed, Embase, Web of Science, and IEEE Xplore databases were searched to identify articles pertaining to the use of machine learning in pharmacovigilance published from the year 2000 to September 2021. After manual screening of 7744 abstracts, a total of 393 papers met the inclusion criteria for further analysis. Extraction of key data on study design, data sources, sample size, and machine learning methodology was performed. Studies with the characteristics of good machine learning practice were defined and manual review focused on identifying studies that fulfilled these criteria and results that showed promise. Results The majority of studies (53%) were focused on detecting safety signals using traditional statistical methods. Of the studies that used more recent machine learning methods, 61% used off-the-shelf techniques with minor modifications. Temporal analysis revealed that newer methods such as deep learning have shown increased use in recent years. We found only 42 studies (10%) that reflect current best practices and trends in machine learning. In the subset of 154 papers that focused on data intake and ingestion, 30 (19%) were found to incorporate the same best practices. Conclusion Advances from artificial intelligence have yet to fully penetrate pharmacovigilance, although recent studies show signs that this may be changing. Supplementary Information The online version contains supplementary material available at 10.1007/s40264-022-01176-1.

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“…The burden of ADR contributes to an overall increase in the cost of hospitalization, with a prevalence of 6.5%, with ADR contributing to 80% of hospitalization [ 28 ]. Studies on using artificial intelligence (AI) to leverage the effect of ADR are currently in their infancy and need more robust studies [ 29 ]. ADR.…”

Section: Reviewmentioning

confidence: 99%

A Narrative Review of Adverse Event Detection, Monitoring, and Prevention in Indian Hospitals

Verman¹,

Anjankar²

2022

Cureus

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An adverse event is any abnormal clinical finding associated with the use of a therapy. Adverse events are classified by reporting an event's seriousness, expectedness, and relatedness. Monitoring patient safety is of utmost importance as more and more data becomes available. In reality, very low numbers of adverse events are reported via the official path. Chart review, voluntary reporting, computerized surveillance, and direct observation can detect adverse drug events. Medication errors are commonly seen in hospitals and need provider and system-based interventions to prevent them. The need of the hour in India is to develop and implement medication safety best practices to avoid adverse events. The utility of artificial intelligence techniques in adverse event detection remains unexplored, and their accuracy and precision need to be studied in a controlled setting. There is a need to develop predictive models to assess the likelihood of adverse reactions while testing novel pharmaceutical drugs.

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Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review

Cited by 32 publications

References 87 publications

An Artificial Intelligence Approach to Support Detection of Neonatal Adverse Drug Reactions Based on Severity and Probability Scores: A New Risk Score as Web-Tool

An Artificial Intelligence Approach to Support Detection of Neonatal Adverse Drug Reactions Based on Severity and Probability Scores: A New Risk Score as Web-Tool

Artificial Intelligence Based on Machine Learning in Pharmacovigilance: A Scoping Review

A Narrative Review of Adverse Event Detection, Monitoring, and Prevention in Indian Hospitals

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