Machine Learning Approaches to Retrieve High-Quality, Clinically Relevant Evidence From the Biomedical Literature: Systematic Review

11 September, 2021

(with thanks to Irina Ibraghimova and LRC Network)

CITATION: JMIR Med Inform. 2021 Sep 9;9(9):e30401. doi: 10.2196/30401.

Machine Learning Approaches to Retrieve High-Quality, Clinically Relevant Evidence From the Biomedical Literature: Systematic Review.

Abdelkader W et al.

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: The goal of the research was 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: Machine learning approaches perform well in retrieving high-quality clinical studies. Performance may improve by applying more sophisticated approaches such as active learning and unsupervised machine learning approaches.

DOI: 10.2196/30401 PMID: 34499041

Comment (NPW): The text includes a description of search filters that I found helpful (even though I am still rather a novice and gratefully rely on HIFA volunteers, especially John Eyers, to help on HIFA literature searches): 'Search filters, also referred to as hedges, allow researchers, clinicians, and librarians to retrieve evidence from bibliographic databases and journals by filtering searches to return reliable and specific articles to address clinical questions, produce systematic reviews, or inform clinical guidelines [5]. MEDLINE search filters, for example, enable researchers to combine the use of free text with controlled vocabularies like Medical Subject Heading (MeSH) terms and other indexing features to improve search results targeting the clinical question at hand [6,7]. There are search filters that focus on the purpose of a study and its methods or topical content areas [8]. Topical search filters help identify articles based on particular clinical conditions using terms related to that condition [8], while methodological search filters comprise terms that identify articles based on their research purpose [9].'

Neil Pakenham-Walsh, HIFA Coordinator,