ArticleInfection and drug resistance2022
Rapid Detection of Carbapenem-Resistant
Article in Infection and drug resistance, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review of machine learning on clinical MALDI-TOF MS.Briefings in bioinformatics · 2026Pooled it
- Artificial intelligence in antimicrobial stewardship: a systematic review and meta-analysis of predictive performance and diagnostic accuracy.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2025Pooled it
- Artificial Intelligence and Bioengineering Approaches for Antimicrobial Resistance Prediction.Medicina (Kaunas, Lithuania) · 2026Review
- Rapid identification of carbapenem-resistant Acinetobacter baumannii based on MALDI-TOF mass spectrometry and machine learning.BMC microbiology · 2026Article
- Prediction of KPC-producingJournal of clinical microbiology · 2026Article
- Machine Learning Model for Predicting Multidrug Resistance in ClinicalDiagnostics (Basel, Switzerland) · 2026Article
- AI-powered rapid detection of multidrug-resistantBioinformatics advances · 2026Article
- Artificial Intelligence for Antimicrobial Resistance Detection and Prediction inInfection and drug resistance · 2026Review
- Development and Validation of an Interpretable Machine Learning Model for Predicting Carbapenem-ResistantInfection and drug resistance · 2026Article
- Rapid detection of antibiotic resistance in Burkholderia pseudomallei using MALDI-TOF mass spectrometry.Scientific reports · 2025Article
- Rapid Identification and Typing of Carbapenem-Resistant Klebsiella pneumoniae Using MALDI-TOF MS and Machine Learning.Microbial biotechnology · 2025Article
- Classification ofMicrobiology spectrum · 2024Article
- From Data to Decisions: Leveraging Artificial Intelligence and Machine Learning in Combating Antimicrobial Resistance - a Comprehensive Review.Journal of medical systems · 2024Review
- Carbapenem-resistant Gram-negative bacteria (CR-GNB) in ICUs: resistance genes, therapeutics, and prevention - a comprehensive review.Frontiers in public health · 2024Review
- Machine learning model of imipenem-resistantHealth science reports · 2023Article
- Differentiation betweenInternational journal of molecular sciences · 2023Article
- Rapid identification of carbapenem-resistant Klebsiella pneumoniae based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry and an artificial neural network model.Journal of biomedical science · 2023Article
- Rapid discrimination ofFrontiers in microbiology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Rapid detection of carbapenem-resistant Methods: Clinically collected Results: The RF, SVM and SVM-K models showed good classification performance with 0.88, 0.88, and 0.91 accuracy, 0.82, 0.85, and 0.89 sensitivity, 0.93, 0.92, and 0.94 specificity with an AUC of 0.9013, 0.9298, and 0.9356, respectively. For the SVM-K model, the optimal dimension reduction was 105 to 153, and the average accuracy was >0.9. The top 10 peak features of significance according to the RF algorithm with 6515 Da appeared in 56.8% of CRKP isolates and 5.3% of CSKP isolates, which indicated the best classification performance. Conclusion: The three RF, SVM, and SVM-K models showed excellent classification performance differentiating the CRKP from CSKP; the SVM-K model was the best. Data analysis with machine learning combined with MALDI-TOF MS can be employed as a rapid and inexpensive alternative to existing detection methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.