Abstract
Aim: To summarize the evidence on the performance of artificial intelligence vs. traditional pharmacoepidemiological techniques. Methods: Ovid MEDLINE (01/1950 to 05/2019) was searched to identify observational studies, meta-analyses, and clinical trials using artificial intelligence techniques having a drug as the exposure or the outcome of the study. Only studies with an available full text in the English language were evaluated. Results: In all, 72 original articles and five reviews were identified via Ovid MEDLINE of which 19 (26.4%) compared the performance of artificial intelligence techniques with traditional pharmacoepidemiological methods. In total, 44 comparisons have been performed in articles that aimed at 1) predicting the needed dosage given the patient’s characteristics (31.8%), 2) predicting the clinical response following a pharmacological treatment (29.5%), 3) predicting the occurrence/severity of adverse drug reactions (20.5%), 4) predicting the propensity score (9.1%), 5) identifying subpopulation more at risk of drug inefficacy (4.5%), 6) predicting drug consumption (2.3%), and 7) predicting drug-induced lengths of stay in hospital (2.3%). In 22 out of 44 (50.0%) comparisons, artificial intelligence performed better than traditional pharmacoepidemiological techniques. Random forest (seven out of 11 comparisons; 63.6%) and artificial neural network (six out of 10 comparisons; 60.0%) were the techniques that in most of the comparisons outperformed traditional pharmacoepidemiological methods. Conclusion: Only a small fraction of articles compared the performance of artificial intelligence techniques with traditional pharmacoepidemiological methods and not all artificial intelligence techniques have been compared in a Pharmacoepidemiological setting. However, in 50% of comparisons, artificial intelligence performed better than pharmacoepidemiological techniques.
Highlights
In the first part of this systematic review (Sessa et al, 2020), we showed that in the past decade there was increased use of machine learning techniques in Pharmacoepidemiology, which is defined by the International Society of Pharmacoepidemiology, as “the discipline studying the utilization and effects of drugs in large numbers of people.” In this discipline, machine learning techniques have been applied mainly on secondary data and mostly to predict the clinical response following a pharmacological treatment, the occurrence/severity of adverse drug reactions, or the needed dosage of drugs with a narrow therapeutic index
For consistency with the first part of this systematic review, we categorized the purpose of using machine learning techniques as follows: 1) To predict clinical response following a pharmacological treatment; 2) To predict the needed dosage given the patient’s characteristics; 3) To predict the occurrence/severity of adverse drug reactions; 4) To predict diagnosis leading to a drug prescription; 5) To predict drug consumption, 6) To predict the propensity score; 7) To predict drug-induced lengths of stay in hospital; 8) To predict adherence to pharmacological treatments; 9) To optimize treatment regimen; 10) To identify subpopulation more at risk of drug inefficacy, and 11) To predict drug-drug interactions
72 original articles and five reviews were identified via Ovid MEDLINE of which 19 (26.4%) compared the performance of artificial intelligence techniques with traditional pharmacoepidemiological methods
Summary
In the first part of this systematic review (Sessa et al, 2020), we showed that in the past decade there was increased use of machine learning techniques in Pharmacoepidemiology, which is defined by the International Society of Pharmacoepidemiology, as “the discipline studying the utilization and effects of drugs in large numbers of people.” In this discipline, machine learning techniques have been applied mainly on secondary data and mostly to predict the clinical response following a pharmacological treatment, the occurrence/severity of adverse drug reactions, or the needed dosage of drugs with a narrow therapeutic index. In the first part of this systematic review (Sessa et al, 2020), we showed that in the past decade there was increased use of machine learning techniques in Pharmacoepidemiology, which is defined by the International Society of Pharmacoepidemiology, as “the discipline studying the utilization and effects of drugs in large numbers of people.” In this discipline, machine learning techniques have been applied mainly on secondary data and mostly to predict the clinical response following a pharmacological treatment, the occurrence/severity of adverse drug reactions, or the needed dosage of drugs with a narrow therapeutic index. The objective of the second part of this systematic review is to provide a detailed overview of articles performing such a comparison
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