Machine Learning Techniques for Antimicrobial Resistance Prediction of <i>Pseudomonas Aeruginosa</i> from Whole Genome Sequence Data

Noman, Sohail M.; Zeeshan, Muhammad; Arshad, Jehangir; Amentie, Melkamu Deressa; Shafiq, Muhammad; Yuan, Yumeng; Zeng, Mi; Li, Xin; Xie, Qingdong; Jiao, Xiaoyang

doi:10.1155/2023/5236168

Cited by 2 publications

(1 citation statement)

References 59 publications

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“…This can be further developed to predict multi-drug resistance in pathogens [48] (Table 1). Machine learning techniques were adopted to support research into bacterial resistance to a panel of antimicrobials using whole-genome sequence data of Pseudomonas aeruginosa, with more than 95% accuracy [49] (Table 1). Furthermore, a similar system was used for the identification of methicillin resistance of Staphylococcus aureus, with an accuracy of 87.6%, sensitivity of 91.8%, and specificity of 83.3% [50] (Table 1).…”

Section: Genome Analysis For Prediction Of Resistant Strains and Susc...mentioning

confidence: 99%

Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence

Rusic,

Kumric,

Seselja Perisin

et al. 2024

Microorganisms

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Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs’ kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.

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Section: Genome Analysis For Prediction Of Resistant Strains and Susc...mentioning

confidence: 99%

Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence

Rusic,

Kumric,

Seselja Perisin

et al. 2024

Microorganisms

View full text Add to dashboard Cite

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Machine learning-based antibiotic resistance prediction models: An updated systematic review and meta-analysis

Lv,

Wang

2024

THC

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BACKGROUND: The widespread use of antibiotics has led to a gradual adaptation of bacteria to these drugs, diminishing the effectiveness of treatments. OBJECTIVE: To comprehensively assess the research progress of antibiotic resistance prediction models based on machine learning (ML) algorithms, providing the latest quantitative analysis and methodological evaluation. METHODS: Relevant literature was systematically retrieved from databases, including PubMed, Embase and the Cochrane Library, from inception up to December 2023. Studies meeting predefined criteria were selected for inclusion. The prediction model risk of bias assessment tool was employed for methodological quality assessment, and a random-effects model was utilised for meta-analysis. RESULTS: The systematic review included a total of 22 studies with a combined sample size of 43,628; 10 studies were ultimately included in the meta-analysis. Commonly used ML algorithms included random forest, decision trees and neural networks. Frequently utilised predictive variables encompassed demographics, drug use history and underlying diseases. The overall sensitivity was 0.57 (95% CI: 0.42–0.70; p< 0.001; I2= 99.7%), the specificity was 0.95 (95% CI: 0.79–0.99; p< 0.001; I2 = 99.9%), the positive likelihood ratio was 10.7 (95% CI: 2.9–39.5), the negative likelihood ratio was 0.46 (95% CI: 0.34–0.61), the diagnostic odds ratio was 23 (95% CI: 7–81) and the area under the receiver operating characteristic curve was 0.78 (95% CI: 0.74–0.81; p< 0.001), indicating a good discriminative ability of ML models for antibiotic resistance. However, methodological assessment and funnel plots suggested a high risk of bias and publication bias in the included studies. CONCLUSION: This meta-analysis provides a current and comprehensive evaluation of ML models for predicting antibiotic resistance, emphasising their potential application in clinical practice. Nevertheless, stringent research design and reporting are warranted to enhance the quality and credibility of future studies. Future research should focus on methodological innovation and incorporate more high-quality studies to further advance this field.

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Machine Learning Techniques for Antimicrobial Resistance Prediction of Pseudomonas Aeruginosa from Whole Genome Sequence Data

Cited by 2 publications

References 59 publications

Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence

Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence

Machine learning-based antibiotic resistance prediction models: An updated systematic review and meta-analysis

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