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

被引:5
|
作者
Rusic, Doris [1 ]
Kumric, Marko [2 ,3 ]
Perisin, Ana Seselja [1 ]
Leskur, Dario [1 ]
Bukic, Josipa [1 ]
Modun, Darko [1 ]
Vilovic, Marino [2 ,3 ]
Vrdoljak, Josip [2 ,3 ]
Martinovic, Dinko [2 ,4 ]
Grahovac, Marko [5 ]
Bozic, Josko [2 ,3 ]
机构
[1] Univ Split, Sch Med, Dept Pharm, Soltanska 2, Split 21000, Croatia
[2] Univ Split, Sch Med, Dept Pathophysiol, Soltanska 2A, Split 21000, Croatia
[3] Univ Split, Sch Med, Lab Cardiometab Res, Soltanska 2A, Split 21000, Croatia
[4] Univ Hosp Split, Dept Maxillofacial Surg, Spinciceva 1, Split 21000, Croatia
[5] Univ Split, Sch Med, Dept Pharmacol, Soltanska 2A, Split 21000, Croatia
基金
英国科研创新办公室;
关键词
artificial intelligence; machine learning; antimicrobial resistance; drug discovery; ANTIBIOTIC-RESISTANCE; PREDICTION; IDENTIFICATION; PEPTIDES; IMPROVE; COLI; TIME;
D O I
10.3390/microorganisms12050842
中图分类号
Q93 [微生物学];
学科分类号
071005 ; 100705 ;
摘要
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.
引用
收藏
页数:25
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