RESUMO
Objective: At the forefront of machine learning research since its inception has been natural language processing, also known as text mining, referring to a wide range of statistical processes for analyzing textual data and retrieving information. In medical fields, text mining has made valuable contributions in unexpected ways, not least by synthesizing data from disparate biomedical studies. This rapid scoping review examines how machine learning methods for text mining can be implemented at the intersection of these disparate fields to improve the workflow and process of conducting systematic reviews in medical research and related academic disciplines. Methods: The primary research question that this investigation asked, "what impact does the use of machine learning have on the methods used by systematic review teams to carry out the systematic review process, such as the precision of search strategies, unbiased article selection or data abstraction and/or analysis for systematic reviews and other comprehensive review types of similar methodology?" A literature search was conducted by a medical librarian utilizing multiple databases, a grey literature search and handsearching of the literature. The search was completed on December 4, 2020. Handsearching was done on an ongoing basis with an end date of April 14, 2023. Results: The search yielded 23,190 studies after duplicates were removed. As a result, 117 studies (1.70%) met eligibility criteria for inclusion in this rapid scoping review. Conclusions: There are several techniques and/or types of machine learning methods in development or that have already been fully developed to assist with the systematic review stages. Combined with human intelligence, these machine learning methods and tools provide promise for making the systematic review process more efficient, saving valuable time for systematic review authors, and increasing the speed in which evidence can be created and placed in the hands of decision makers and the public.
