Exploratory pharmacovigilance with machine learning in big patient data: A focused scoping review

Abstract

Background: Machine learning can operationalize the rich and complex data in electronic patient records for exploratory pharmacovigilance endeavours.

Objective: The objective of this review is to identify applications of machine learning and big patient data in exploratory pharmacovigilance.

Methods: We searched PubMed and Embase and included original articles with an exploratory pharmacovigilance purpose, focusing on medicinal interventions and reporting the use of machine learning in electronic patient records with ≥1000 patients collected after market entry.

Findings: Of 2557 studies screened, seven were included. Those covered six countries and were published between 2015 and 2021. The most prominent machine learning methods were random forests, logistic regressions, and support vector machines. Two studies used artificial neural networks or naive Bayes classifiers. One study used formal concept analysis for association mining, and another used temporal difference learning. Five studies compared several methods against each other. The numbers of patients in most data sets were in the order of thousands; two studies used what can more reasonably be considered big data with >1 000 000 patients records.

Conclusion: Despite years of great aspirations for combining machine learning and clinical data for exploratory pharmacovigilance, only few studies still seem to deliver somewhat on these expectations.

Keywords: administrative pharmacology; adverse drug reactions; machine learning; pharmacoepidemiology; postmarketing surveillance.