综述：病原菌耐药性数据挖掘模型及预测方法研究,The Journal of Antibi
综述：病原菌耐药性数据挖掘模型及预测方法研究
The Journal of Antibiotics
(
IF
2.1
)
Pub Date : 2021-09-14
, DOI:
10.1038/s41429-021-00471-w
Xinxing Li
1
,
Ziyi Zhang
1
,
Buwen Liang
1
,
Fei Ye
2
,
Weiwei Gong
2
Affiliation
College of Information and Electrical Engineering, China Agricultural University, Beijing, PR China.
Transportation and Economics Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing, PR China.
抗微生物药物为上个世纪的医学和社会发展铺平了道路，老人护理13825404095对于治疗人类和动物的感染是不可或缺的。抗生素耐药病原体的急剧传播和多样性显着降低了基本上所有抗生素类别的功效，并且是影响人类和动物健康的全球性问题。抗菌药物耐药性受耐药基因、给药剂量等复杂因素的影响，具有高度非线性、时滞性和多变量耦合性，耐药性数据量大且冗余，难以预测和分析。基于机器学习方法和数据挖掘技术，本文综述了（1）抗菌素耐药性数据存储和分析技术，(2) 抗菌素耐药性评估方法和相关的抗菌素耐药性风险评估方法，以及 (3) 抗菌素耐药性预测方法。最后总结了当前抗菌素耐药性的研究成果及发展趋势，为抗菌素耐药性研究提供系统、全面的参考。
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A review: antimicrobial resistance data mining models and prediction methods study for pathogenic bacteria
Antimicrobials have paved the way for medical and social development over the last century and are indispensable for treating infections in humans and animals. The dramatic spread and diversity of antibiotic-resistant pathogens have significantly reduced the efficacy of essentially all antibiotic classes and is a global problem affecting human and animal health. Antimicrobial resistance is influenced by complex factors such as resistance genes and dosing, which are highly nonlinear, time-lagged and multivariate coupled, and the amount of resistance data is large and redundant, making it difficult to predict and analyze. Based on machine learning methods and data mining techniques, this paper reviews (1) antimicrobial resistance data storage and analysis techniques, (2) antimicrobial resistance assessment methods and the associated risk assessment methods for antimicrobial resistance, and (3) antimicrobial resistance prediction methods. Finally, the current research results on antimicrobial resistance and the development trend are summarized to provide a systematic and comprehensive reference for the research on antimicrobial resistance.