Machine learning-assisted literature screening for a medication-use process-related systematic review.
Journal:
American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists
Published Date:
May 21, 2025
Abstract
PURPOSE: This article summarizes a novel methodology of applying machine learning (ML) algorithms trained with external training data to assist with article screening for 2 annual review series related to the medication-use process (MUP) generally and the MUP in ambulatory care settings (ACMUP) specifically. As the literature review for these 2 series grew over time, it became essential for the authors to develop methods to be efficient while still capturing most of the relevant literature. The ML model can be used to predict whether search results are likely to be relevant or not relevant. Results least likely to be relevant can then be excluded without manual screening, allowing research teams to save time that would otherwise be spent reviewing a portion of the search results for inclusion. ML models require a large training dataset typically derived from the unclassified corpus. In this study, the authors demonstrate the efficacy of training the ML model using external training data, which is possible in scenarios such as a systematic review update or ongoing review series such as those for the MUP and ACMUP.