A rapid approach with machine learning for quantifying the relative burden of antimicrobial resistance in natural aquatic environments.

Autor: Jiang P; Department of Industrial Engineering and Management, Business School, Sichuan University, Chengdu 610064, China; NUS Environmental Research Institute, National University of Singapore, Singapore 117411, Singapore. Electronic address: pengjiang@scu.edu.cn., Sun S; Department of Industrial Engineering and Management, Business School, Sichuan University, Chengdu 610064, China; Department of Industrial Systems Engineering & Management, National University of Singapore, Singapore 119260, Singapore., Goh SG; NUS Environmental Research Institute, National University of Singapore, Singapore 117411, Singapore., Tong X; NUS Environmental Research Institute, National University of Singapore, Singapore 117411, Singapore., Chen Y; School of Resources and Environmental Engineering, Hefei University of Technology, Hefei, 230009, China., Yu K; School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China., He Y; School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China., Gin KY; NUS Environmental Research Institute, National University of Singapore, Singapore 117411, Singapore; Department of Civil & Environmental Engineering, National University of Singapore, Singapore 117576, Singapore. Electronic address: ceeginyh@nus.edu.sg.
Jazyk: angličtina
Zdroj: Water research [Water Res] 2024 Sep 15; Vol. 262, pp. 122079. Date of Electronic Publication: 2024 Jul 10.
DOI: 10.1016/j.watres.2024.122079
Abstrakt: The massive use and discharge of antibiotics have led to increasing concerns about antimicrobial resistance (AMR) in natural aquatic environments. Since the dose-response mechanisms of pathogens with AMR have not yet been fully understood, and the antibiotic resistance genes and bacteria-related data collection via field sampling and laboratory testing is time-consuming and expensive, designing a rapid approach to quantify the burden of AMR in the natural aquatic environment has become a challenge. To cope with such a challenge, a new approach involving an integrated machine-learning framework was developed by investigating the associations between the relative burden of AMR and easily accessible variables (i.e., relevant environmental variables and adjacent land-use patterns). The results, based on a real-world case analysis, demonstrate that the quantification speed has been reduced from 3-7 days, which is typical for traditional measurement procedures with field sampling and laboratory testing, to approximately 0.5 hours using the new approach. Moreover, all five metrics for AMR relative burden quantification exceed the threshold level of 85%, with F1-score surpassing 0.92. Compared to logistic regression, decision trees, and basic random forest, the adaptive random forest model within the framework significantly improves quantification accuracy without sacrificing model interpretability. Two environmental variables, dissolved oxygen and resistivity, along with the proportion of green areas were identified as three key feature variables for the rapid quantification. This study contributes to the enrichment of burden analyses and management practices for rapid quantification of the relative burden of AMR without dose-response information.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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