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Machine learning in predicting antimicrobial resistance: a systematic review and meta-analysis

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收录情况： ◇ SCIE

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机构： [1]Department of Pharmacy, West China Hospital, Sichuan University, Chengdu, China. Electronic address: 442359065@qq.com. [2]Department of Pain Medicine, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China. [3]Department of Pharmacy, People's Hospital of Xinjin District, Chengdu, China. [4]Department of pharmacy, West China Second University Hospital, Sichuan university, Chengdu, China. [5]Department of Medical Big Data Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.

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关键词： antimicrobial resistance machine learning risk score prediction

摘要：

Antimicrobial resistance (AMR) is a global health threat, rapid and timely identification of AMR improves patient prognosis and reduces inappropriate antibiotic use. We systematically searched relevant literature in PubMed, Web of Science, Embase and Institute of Electrical and Electronics Engineers prior to Sep 28, 2021. The study that deployed machine learning or risk score as tool to predict AMR was included in the final review. There were 25 studies that employed the ML algorithm to predict AMR. ESBL, MRSA and carbapenem resistance were the most common outcomes in studies with a specific resistance pattern. The most common algorithms in ML prediction were logistic regression (n=14 studies), decision tree (n=14) and random forest (n=7). The area under the curve (AUC) range for ML prediction is 0.48-0.93. The pooled AUC for ML prediction is 0.82(0.78-0.85). Compared with risk score, higher specificity [87% (82-91) vs. 37% (25-51)] is indicated for ML prediction, but not sensitivity [67% (62-72) vs. 73% (67-79)]. ML might be a potential technology for AMR prediction. However, retrospective methodology for model development, nonstandard data processing and scarcity of validation in a randomized controlled trial or real-world study limit the application of these models in clinical practice.Copyright © 2022. Published by Elsevier Ltd.

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出版当年[2021]版：

大类 | 2 区医学

小类 | 2 区传染病学 2 区药学 3 区微生物学

最新[2025]版：

大类 | 2 区医学

小类 | 1 区药学 2 区传染病学 2 区微生物学

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出版当年[2020]版：

Q1 INFECTIOUS DISEASES Q1 PHARMACOLOGY & PHARMACY Q2 MICROBIOLOGY

最新[2023]版：

Q1 INFECTIOUS DISEASES Q1 MICROBIOLOGY Q1 PHARMACOLOGY & PHARMACY

影响因子： 4.9 最新[2023版] 5.6 最新五年平均 5.283 出版当年[2020版] 5.209 出版当年五年平均 4.621 出版前一年[2019版] 15.441 出版后一年[2021版]

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第一作者机构： [1]Department of Pharmacy, West China Hospital, Sichuan University, Chengdu, China. Electronic address: 442359065@qq.com.

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