JMIR Publications

ABSTRACT

Background:

The rapid growth of the biomedical literature makes identifying strong evidence a time-consuming task. Applying machine learning to the process could be a viable solution that limits effort while maintaining accuracy.

Objective:

To summarize the nature and comparative performance of machine learning approaches that have been applied to retrieve high-quality evidence for clinical consideration from the biomedical literature.

Methods:

We conducted a systematic review of studies that applied machine learning techniques to identify high-quality clinical articles in the biomedical literature. Multiple databases were searched to July 2020. Extracted data focused on the applied machine learning model, steps in the development of the models, and model performance.

Results:

From 3918 retrieved studies, 10 met our inclusion criteria. All followed a supervised machine learning approach and applied, from a limited range of options, a high-quality standard for the training of their model. The results show that machine learning can achieve a sensitivity of 95% while maintaining a high precision of 86%.

Conclusions:

Applying machine learning to distinguish studies with strong evidence for clinical care has the potential to decrease the workload of manually identifying these. The evidence base is active and evolving. Reported methods were variable across the studies but focused on supervised machine learning approaches. Performance may improve by applying more sophisticated approaches such as active learning, auto-machine learning, and unsupervised machine learning approaches.