首页|Universite Paris Cite Reports Findings in Antibiotics (Machine learn- ing to predict antimicrobial resistance: future applications in clinical practice?)
Universite Paris Cite Reports Findings in Antibiotics (Machine learn- ing to predict antimicrobial resistance: future applications in clinical practice?)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
New research on Drugs and Therapies - Antibiotics is the subject of a report. According to news reporting originating from Paris, France, by NewsRx correspondents, research stated, "Machine learning (ML) is increasingly being used to predict antimicrobial resistance (AMR). This review aims to provide physicians with an overview of the literature on ML as a means of AMR prediction." Our news editors obtained a quote from the research from Universite Paris Cite, "References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, ACM Digital Library, and IEEE Xplore Digital Library up to December 2023. Thirty-six studies were included in this review. Thirty-two studies (32/36, 89%) were based on hospital data and four (4/36, 11%) on outpatient data. The vast majority of them were conducted in high-resource settings (33/36, 92%). Twenty-four (24/36, 67%) studies developed systems to predict drug resistance in infected patients, eight (n=8/36, 22%) tested the performances of ML-assisted antibiotic prescription, two (n=2/36, 6%) assessed ML per- formances in predicting colonization with carbapenem-resistant bacteria and, finally, two assessed national and international AMR trends. The most common inputs were demographic characteristics (25/36, 70%), previous antibiotic susceptibility testing (19/36, 53%) and prior antibiotic exposure (15/36, 42%). Thirty- three (92%) studies targeted prediction of Gram-negative bacteria (GNB) resistance as an output (92%). The studies included showed moderate to high performances, with AUROC ranging from 0.56 to 0.93. ML can potentially provide valuable assistance in AMR prediction."
ParisFranceEuropeAntibacterialsAntibioticsAntimicro- bial ResistanceAntimicrobialsCyborgsDrug ResistanceDrugs and TherapiesEmerging TechnologiesHealth and MedicineMachine Learning
NETL
NSTL
