Text mining and topic modeling insights on fish welfare and antimicrobial use in aquaculture

Antimicrobial use (AMU) and antibiotic resistance (AR) in aquaculture present growing concerns for public health. Furthermore, there exists a correlation between fishes' welfare and AMU. This systematic review aims to analyze the scientific literature on fishes' welfare and AMU/AR over the last 32 years, identifing the main research topics, and the fields where investigation has been imitated. A comprehensive search was conducted using Scopus, employing specific keywords related to AMU/AR and welfare and preselected filters. The study employed a systematic approach following the PRISMA guidelines, and machine learning techniques were used. From 2,019 records retrieved, only those focused-on fishes welfare and AMU/AR were retained. Ultimately, 185 records showing a connection between these topics were included in the qualitative analysis. Text mining analysis revealed terms with the highest weighted frequency in the data corpus, while topic analysis identified the top five core areas: Topic 1 (Antibiotic resistance and strain genetic isolation), Topic 2 (Aquaculture and Human Health, environment, and food), Topic 3 (Fish response to stress and indicators), Topic 4 (Control of water and fish growth), and Topic 5 (Aquaculture research and current farming methods). The results indicate a growing interest in fish welfare and AMU/AR, while also highlighting areas that require further investigation, such as the link between these research fields. Improving fish welfare can reduce AR, aligning with the One Health policy.