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

抗生素 荟萃分析 适应(眼睛) 抗生素耐药性 计算机科学 机器学习 人工智能 重症监护医学 医学 微生物学 生物 内科学 神经科学
作者
Guodong Lv,Yuntao Wang
出处
期刊:Technology and Health Care [IOS Press]
卷期号:: 1-18 被引量:2
标识
DOI:10.3233/thc-240119
摘要

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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