Natural language processing for detecting adverse drug events: A systematic review protocol.

Abstract

Background: Detecting Adverse Drug Events (ADEs) is an emerging research area, attracting great interest in the research community. Better anticipatory management of predisposing factors has considerable potential to improve outcomes. Automatic extraction of ADEs using Natural Language Processing (NLP) has a great potential to significantly facilitate efficient and effective distillation of such knowledge, to better understand and predict risk of adverse events.

Methods: This systematic review follows the six-stage including the literature from 6 databases (Embase, Medline, Web Of Science Core Collection, ACM Guide to Computing Literature, IEEE Digital Library and Scopus). Following the title, abstract and full-text screenings, characteristics and main findings of the included studies and resources will be tabulated and summarized. The risk of bias and reporting quality was assessed using the PROBAST tool.

Results: We developed our search strategy and collected all relevant publications. As of December 2024, we have completed all the stages of the systematic review. We identified 178 studies for inclusion through the academic literature search (where data was extracted from all of the papers). Right now, we are writing up the systematic review paper where we are synthesising the different findings. Further refinement of the eligibility criteria and data extraction has been ongoing since August 2022.

Conclusion: In this systematic review, we will identify and consolidate information and evidence related to the use and effectiveness of existing NLP approaches and tools for automatically detecting ADEs from free text (discharge summaries, General Practitioner notes, social media, etc.). Our findings will improve the understanding of the current landscape of the use of NLP for extracting ADEs. It will lead to better anticipatory management of predisposing factors with the potential to improve outcomes considerably. Our results will also be valuable both to NLP researchers developing methods to extract ADEs and to translational/clinical researchers who use NLP for this purpose and in healthcare in general. For example, from our initial analysis of the studies, we can conclude that the majority of the proposed works are about the detection (extraction) of ADEs from text. An important portion of studies also focus on the binary classification of text (for highlighting if it includes or not ADEs). Different challenges related to the unbalanced dataset, abbreviations and acronyms but also to the lower results with rare ADEs were also mentioned by the studied papers.