Machine learning in medication prescription: A systematic review

Iancu A, Leb I, Prokosch HU, Rödle W (2023)

Publication Type: Journal article, Review article

Publication year: 2023


Book Volume: 180

Article Number: 105241

DOI: 10.1016/j.ijmedinf.2023.105241


Background: Medication prescription is a complex process that could benefit from current research and development in machine learning through decision support systems. Particularly pediatricians are forced to prescribe medications “off-label” as children are still underrepresented in clinical studies, which leads to a high risk of an incorrect dose and adverse drug effects. Methods: PubMed, IEEE Xplore and PROSPERO were searched for relevant studies that developed and evaluated well-performing machine learning algorithms following the PRISMA statement. Quality assessment was conducted in accordance with the IJMEDI checklist. Identified studies were reviewed in detail, including the required variables for predicting the correct dose, especially of pediatric medication prescription. Results: The search identified 656 studies, of which 64 were reviewed in detail and 36 met the inclusion criteria. According to the IJMEDI checklist, five studies were considered to be of high quality. 19 of the 36 studies dealt with the active substance warfarin. Overall, machine learning algorithms based on decision trees or regression methods performed superior regarding their predictive power than algorithms based on neural networks, support vector machines or other methods. The use of ensemble methods like bagging or boosting generally enhanced the accuracy of the dose predictions. The required input and output variables of the algorithms were considerably heterogeneous and differ strongly among the respective substance. Conclusions: By using machine learning algorithms, the prescription process could be simplified and dosing correctness could be enhanced. Despite the heterogenous results among the different substances and cases and the lack of pediatric use cases, the identified approaches and required variables can serve as an excellent starting point for further development of algorithms predicting drug doses, particularly for children. Especially the combination of physiologically-based pharmacokinetic models with machine learning algorithms represents a great opportunity to enhance the predictive power and accuracy of the developed algorithms.

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How to cite


Iancu, A., Leb, I., Prokosch, H.-U., & Rödle, W. (2023). Machine learning in medication prescription: A systematic review. International Journal of Medical Informatics, 180.


Iancu, Alexa, et al. "Machine learning in medication prescription: A systematic review." International Journal of Medical Informatics 180 (2023).

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